diff --git a/README.md b/README.md
index 3243f3ce24fada59a0b6f509172b3277e080f7aa..03bc26dd47b6dd742ca99da6cd29f9baca4dcc83 100644
--- a/README.md
+++ b/README.md
@@ -1,209 +1,151 @@
-[English](README_en.md) | 简体中文
+English | [简体中文](README_ch.md)
+
+## Introduction
+PaddleOCR aims to create rich, leading, and practical OCR tools that help users train better models and apply them into practice.
+
+**Recent updates**
+- 2020.9.22 Update the PP-OCR technical article, https://arxiv.org/abs/2009.09941
+- 2020.9.19 Update the ultra lightweight compressed ppocr_mobile_slim series models, the overall model size is 3.5M (see [PP-OCR Pipline](#PP-OCR-Pipline)), suitable for mobile deployment. [Model Downloads](#Supported-Chinese-model-list)
+- 2020.9.17 Update the ultra lightweight ppocr_mobile series and general ppocr_server series Chinese and English ocr models, which are comparable to commercial effects. [Model Downloads](#Supported-Chinese-model-list)
+- 2020.9.17 update [English recognition model](./doc/doc_en/models_list_en.md#english-recognition-model) and [Multilingual recognition model](doc/doc_en/models_list_en.md#english-recognition-model), `German`, `French`, `Japanese` and `Korean` have been supported. Models for more languages will continue to be updated.
+- 2020.8.24 Support the use of PaddleOCR through whl package installation，pelease refer [PaddleOCR Package](./doc/doc_en/whl_en.md)
+- 2020.8.21 Update the replay and PPT of the live lesson at Bilibili on August 18, lesson 2, easy to learn and use OCR tool spree. [Get Address](https://aistudio.baidu.com/aistudio/education/group/info/1519)
+- [more](./doc/doc_en/update_en.md)
+
+## Features
+- PPOCR series of high-quality pre-trained models, comparable to commercial effects
+ - Ultra lightweight ppocr_mobile series models: detection (2.6M) + direction classifier (0.9M) + recognition (4.6M) = 8.1M
+ - General ppocr_server series models: detection (47.2M) + direction classifier (0.9M) + recognition (107M) = 155.1M
+ - Ultra lightweight compression ppocr_mobile_slim series models: detection (1.4M) + direction classifier (0.5M) + recognition (1.6M) = 3.5M
+- Support Chinese, English, and digit recognition, vertical text recognition, and long text recognition
+- Support multi-language recognition: Korean, Japanese, German, French
+- Support user-defined training, provides rich predictive inference deployment solutions
+- Support PIP installation, easy to use
+- Support Linux, Windows, MacOS and other systems
+
+## Visualization
-## 简介
-PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力使用者训练出更好的模型，并应用落地。
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大小\(M\) | 检测模型
大小\(M\) | 方向分类器
模型大小\(M\) | 识别模型
大小\(M\) | 整体识别
F\-score |
+|:-:|:-:|:-:|:-:|:-:|:-:|
+| ch\_ppocr\_mobile\_v1\.1 | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.5193 |
+| ch\_ppocr\_server\_v1\.1 | 155\.1 | 47\.2 | 0\.9 | 107 | 0\.5414 |
+| ch\_ppocr\_mobile\_v1\.0 | 8\.6 | 4\.1 | \- | 4\.5 | 0\.393 |
+| ch\_ppocr\_server\_v1\.0 | 203\.8 | 98\.5 | \- | 105\.3 | 0\.4436 |
+
+不同预测模型在T4 GPU上预测速度对比，单位ms
+
+| 模型名称 | 整体 | 检测 | 方向分类器 | 识别 |
+|:-:|:-:|:-:|:-:|:-:|
+| ch\_ppocr\_mobile\_v1\.1 | 137 | 35 | 24 | 78 |
+| ch\_ppocr\_server\_v1\.1 | 204 | 39 | 25 | 140 |
+| ch\_ppocr\_mobile\_v1\.0 | 117 | 41 | \- | 76 |
+| ch\_ppocr\_server\_v1\.0 | 199 | 52 | \- | 147 |
+
+不同预测模型在CPU上预测速度对比，单位ms
+
+| 模型名称 | 整体 | 检测 | 方向分类器 | 识别 |
+|:-:|:-:|:-:|:-:|:-:|
+| ch\_ppocr\_mobile\_v1\.1 | 421 | 164 | 51 | 206 |
+| ch\_ppocr\_mobile\_v1\.0 | 398 | 219 | \- | 179 |
+
+裁剪量化模型和原始模型模型大小，整体识别精度和在SD 855上预测速度对比
+
+| 模型名称 | 整体模型
大小\(M\) | 检测模型
大小\(M\) | 方向分类器
模型大小\(M\) | 识别模型
大小\(M\) | 整体识别
F\-score | SD 855
\(ms\) |
+|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
+| ch\_ppocr\_mobile\_v1\.1 | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.5193 | 306 |
+| ch\_ppocr\_mobile\_slim\_v1\.1 | 3\.5 | 1\.4 | 0\.5 | 1\.6 | 0\.521 | 268 |
diff --git a/doc/doc_ch/config.md b/doc/doc_ch/config.md
index fe8db9c893cf0e6190111de5fe7627d2fe52a4fd..f9c664d4ea38e2e52dc76bfb5b63d9a515b106a7 100644
--- a/doc/doc_ch/config.md
+++ b/doc/doc_ch/config.md
@@ -10,7 +10,7 @@
## 配置文件 Global 参数介绍
-以 `rec_chinese_lite_train.yml` 为例
+以 `rec_chinese_lite_train_v1.1.yml ` 为例
| 字段 | 用途 | 默认值 | 备注 |
@@ -32,6 +32,7 @@
| loss_type | 设置 loss 类型 | ctc | 支持两种loss： ctc / attention |
| distort | 设置是否使用数据增强 | false | 设置为true时，将在训练时随机进行扰动，支持的扰动操作可阅读[img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py) |
| use_space_char | 设置是否识别空格 | false | 仅在 character_type=ch 时支持空格 |
+| label_list | 设置方向分类器支持的角度 | ['0','180'] | 仅在方向分类器中生效 |
| average_window | ModelAverage优化器中的窗口长度计算比例 | 0.15 | 目前仅应用与SRN |
| max_average_window | 平均值计算窗口长度的最大值 | 15625 | 推荐设置为一轮训练中mini-batchs的数目|
| min_average_window | 平均值计算窗口长度的最小值 | 10000 | \ |
diff --git a/doc/doc_ch/customize.md b/doc/doc_ch/customize.md
index 6e471c1c4831f0d98f00b811f18ef12b377ecffa..5944bf08e4c4fac0da5a8e939936719e0385f4e1 100644
--- a/doc/doc_ch/customize.md
+++ b/doc/doc_ch/customize.md
@@ -6,7 +6,7 @@
PaddleOCR提供了EAST、DB两种文本检测算法，均支持MobileNetV3、ResNet50_vd两种骨干网络，根据需要选择相应的配置文件，启动训练。例如，训练使用MobileNetV3作为骨干网络的DB检测模型（即超轻量模型使用的配置）：
```
-python3 tools/train.py -c configs/det/det_mv3_db.yml
+python3 tools/train.py -c configs/det/det_mv3_db.yml 2>&1 | tee det_db.log
```
更详细的数据准备和训练教程参考文档教程中[文本检测模型训练/评估/预测](./detection.md)。
@@ -14,7 +14,7 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml
PaddleOCR提供了CRNN、Rosetta、STAR-Net、RARE四种文本识别算法，均支持MobileNetV3、ResNet34_vd两种骨干网络，根据需要选择相应的配置文件，启动训练。例如，训练使用MobileNetV3作为骨干网络的CRNN识别模型（即超轻量模型使用的配置）：
```
-python3 tools/train.py -c configs/rec/rec_chinese_lite_train.yml
+python3 tools/train.py -c configs/rec/rec_chinese_lite_train.yml 2>&1 | tee rec_ch_lite.log
```
更详细的数据准备和训练教程参考文档教程中[文本识别模型训练/评估/预测](./recognition.md)。
diff --git a/doc/doc_ch/data_annotation.md b/doc/doc_ch/data_annotation.md
index 056fc73bb5f8dd281cf5e77965870210c21c395b..da14b10d3565c8fa7bb06d189035f18eba262821 100644
--- a/doc/doc_ch/data_annotation.md
+++ b/doc/doc_ch/data_annotation.md
@@ -19,3 +19,9 @@
- 工具地址：https://github.com/wkentaro/labelme
- 示意图：
+
+### 4. Vott
+- 工具描述：支持矩形,多边形等图片标注.支持视频标注.方便使用的快捷键以及比较好看的界面.同时支持导出多种标签格式.
+- 工具地址：https://github.com/microsoft/VoTT
+- 示意图：
+ 
diff --git a/doc/doc_ch/detection.md b/doc/doc_ch/detection.md
index 84c90d18a4ac5e1133a8202d574b789848060855..26ee9d8a587b29a62b0f7646ed7d2e8bbaf62883 100644
--- a/doc/doc_ch/detection.md
+++ b/doc/doc_ch/detection.md
@@ -14,6 +14,15 @@ wget -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/train_icdar2015_l
wget -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/test_icdar2015_label.txt
```
+PaddleOCR 也提供了数据格式转换脚本，可以将官网 label 转换支持的数据格式。 数据转换工具在 `train_data/gen_label.py`, 这里以训练集为例：
+
+```
+# 将官网下载的标签文件转换为 train_icdar2015_label.txt
+python gen_label.py --mode="det" --root_path="icdar_c4_train_imgs/" \
+ --input_path="ch4_training_localization_transcription_gt" \
+ --output_label="train_icdar2015_label.txt"
+```
+
解压数据集和下载标注文件后，PaddleOCR/train_data/ 有两个文件夹和两个文件，分别是：
```
/PaddleOCR/train_data/icdar2015/text_localization/
@@ -35,13 +44,15 @@ json.dumps编码前的图像标注信息是包含多个字典的list，字典中
## 快速启动训练
-首先下载模型backbone的pretrain model，PaddleOCR的检测模型目前支持两种backbone，分别是MobileNetV3、ResNet50_vd，
+首先下载模型backbone的pretrain model，PaddleOCR的检测模型目前支持两种backbone，分别是MobileNetV3、ResNet_vd系列，
您可以根据需求使用[PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/master/ppcls/modeling/architectures)中的模型更换backbone。
```shell
cd PaddleOCR/
# 下载MobileNetV3的预训练模型
wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x0_5_pretrained.tar
-# 下载ResNet50的预训练模型
+# 或，下载ResNet18_vd的预训练模型
+wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet18_vd_pretrained.tar
+# 或，下载ResNet50_vd的预训练模型
wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
# 解压预训练模型文件，以MobileNetV3为例
@@ -62,22 +73,25 @@ tar -xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_model
*如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false*
```shell
-python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained/
+# 训练 mv3_db 模型，并将训练日志保存为 tain_det.log
+python3 tools/train.py -c configs/det/det_mv3_db_v1.1.yml \
+ -o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained/ \
+ 2>&1 | tee train_det.log
```
-上述指令中，通过-c 选择训练使用configs/det/det_db_mv3.yml配置文件。
+上述指令中，通过-c 选择训练使用configs/det/det_db_mv3_v1.1.yml配置文件。
有关配置文件的详细解释，请参考[链接](./config.md)。
您也可以通过-o参数在不需要修改yml文件的情况下，改变训练的参数，比如，调整训练的学习率为0.0001
```shell
-python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
+python3 tools/train.py -c configs/det/det_mv3_db_v1.1.yml -o Optimizer.base_lr=0.0001
```
#### 断点训练
如果训练程序中断，如果希望加载训练中断的模型从而恢复训练，可以通过指定Global.checkpoints指定要加载的模型路径：
```shell
-python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./your/trained/model
+python3 tools/train.py -c configs/det/det_mv3_db_v1.1.yml -o Global.checkpoints=./your/trained/model
```
**注意**：`Global.checkpoints`的优先级高于`Global.pretrain_weights`的优先级，即同时指定两个参数时，优先加载`Global.checkpoints`指定的模型，如果`Global.checkpoints`指定的模型路径有误，会加载`Global.pretrain_weights`指定的模型。
@@ -86,17 +100,17 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./you
PaddleOCR计算三个OCR检测相关的指标，分别是：Precision、Recall、Hmean。
-运行如下代码，根据配置文件`det_db_mv3.yml`中`save_res_path`指定的测试集检测结果文件，计算评估指标。
+运行如下代码，根据配置文件`det_db_mv3_v1.1.yml`中`save_res_path`指定的测试集检测结果文件，计算评估指标。
评估时设置后处理参数`box_thresh=0.6`，`unclip_ratio=1.5`，使用不同数据集、不同模型训练，可调整这两个参数进行优化
```shell
-python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="{path/to/weights}/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
+python3 tools/eval.py -c configs/det/det_mv3_db_v1.1.yml -o Global.checkpoints="{path/to/weights}/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
```
训练中模型参数默认保存在`Global.save_model_dir`目录下。在评估指标时，需要设置`Global.checkpoints`指向保存的参数文件。
比如：
```shell
-python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
+python3 tools/eval.py -c configs/det/det_mv3_db_v1.1.yml -o Global.checkpoints="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
```
* 注：`box_thresh`、`unclip_ratio`是DB后处理所需要的参数，在评估EAST模型时不需要设置
@@ -105,16 +119,16 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="./ou
测试单张图像的检测效果
```shell
-python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o TestReader.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy"
+python3 tools/infer_det.py -c configs/det/det_mv3_db_v1.1.yml -o TestReader.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy"
```
测试DB模型时，调整后处理阈值，
```shell
-python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o TestReader.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
+python3 tools/infer_det.py -c configs/det/det_mv3_db_v1.1.yml -o TestReader.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
```
测试文件夹下所有图像的检测效果
```shell
-python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o TestReader.infer_img="./doc/imgs_en/" Global.checkpoints="./output/det_db/best_accuracy"
+python3 tools/infer_det.py -c configs/det/det_mv3_db_v1.1.yml -o TestReader.infer_img="./doc/imgs_en/" Global.checkpoints="./output/det_db/best_accuracy"
```
diff --git a/doc/doc_ch/framework.png b/doc/doc_ch/framework.png
new file mode 100644
index 0000000000000000000000000000000000000000..db151a0e161771f33a438d2e98b49777c34950f8
Binary files /dev/null and b/doc/doc_ch/framework.png differ
diff --git a/doc/doc_ch/inference.md b/doc/doc_ch/inference.md
index 293fee2f4291a8400661de1ed08f0c6807eef977..0432695a3fbb31f04122c4134490ff465e445b4c 100644
--- a/doc/doc_ch/inference.md
+++ b/doc/doc_ch/inference.md
@@ -3,7 +3,7 @@
inference 模型（`fluid.io.save_inference_model`保存的模型）
一般是模型训练完成后保存的固化模型，多用于预测部署。训练过程中保存的模型是checkpoints模型，保存的是模型的参数，多用于恢复训练等。
-与checkpoints模型相比，inference 模型会额外保存模型的结构信息，在预测部署、加速推理上性能优越，灵活方便，适合与实际系统集成。更详细的介绍请参考文档[分类预测框架](https://paddleclas.readthedocs.io/zh_CN/latest/extension/paddle_inference.html).
+与checkpoints模型相比，inference 模型会额外保存模型的结构信息，在预测部署、加速推理上性能优越，灵活方便，适合与实际系统集成。更详细的介绍请参考文档[分类预测框架](https://github.com/PaddlePaddle/PaddleClas/blob/master/docs/zh_CN/extension/paddle_inference.md).
接下来首先介绍如何将训练的模型转换成inference模型，然后将依次介绍文本检测、文本识别以及两者串联基于预测引擎推理。
@@ -11,24 +11,30 @@ inference 模型（`fluid.io.save_inference_model`保存的模型）
- [一、训练模型转inference模型](#训练模型转inference模型)
- [检测模型转inference模型](#检测模型转inference模型)
- [识别模型转inference模型](#识别模型转inference模型)
-
+ - [方向分类模型转inference模型](#方向分类模型转inference模型)
+
- [二、文本检测模型推理](#文本检测模型推理)
- [1. 超轻量中文检测模型推理](#超轻量中文检测模型推理)
- [2. DB文本检测模型推理](#DB文本检测模型推理)
- [3. EAST文本检测模型推理](#EAST文本检测模型推理)
- [4. SAST文本检测模型推理](#SAST文本检测模型推理)
-
+
- [三、文本识别模型推理](#文本识别模型推理)
- [1. 超轻量中文识别模型推理](#超轻量中文识别模型推理)
- [2. 基于CTC损失的识别模型推理](#基于CTC损失的识别模型推理)
- [3. 基于Attention损失的识别模型推理](#基于Attention损失的识别模型推理)
- - [4. 自定义文本识别字典的推理](#自定义文本识别字典的推理)
-
-- [四、文本检测、识别串联推理](#文本检测、识别串联推理)
+ - [4. 基于SRN损失的识别模型推理](#基于SRN损失的识别模型推理)
+ - [5. 自定义文本识别字典的推理](#自定义文本识别字典的推理)
+ - [6. 多语言模型的推理](#多语言模型的推理)
+
+- [四、方向分类模型推理](#方向识别模型推理)
+ - [1. 方向分类模型推理](#方向分类模型推理)
+
+- [五、文本检测、方向分类和文字识别串联推理](#文本检测、方向分类和文字识别串联推理)
- [1. 超轻量中文OCR模型推理](#超轻量中文OCR模型推理)
- [2. 其他模型推理](#其他模型推理)
-
-
+
+
## 一、训练模型转inference模型
@@ -36,7 +42,7 @@ inference 模型（`fluid.io.save_inference_model`保存的模型）
下载超轻量级中文检测模型：
```
-wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar && tar xf ./ch_lite/ch_det_mv3_db.tar -C ./ch_lite/
+wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v1.1_det_train.tar -C ./ch_lite/
```
上述模型是以MobileNetV3为backbone训练的DB算法，将训练好的模型转换成inference模型只需要运行如下命令：
```
@@ -45,7 +51,7 @@ wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar &
# Global.checkpoints参数设置待转换的训练模型地址，不用添加文件后缀.pdmodel，.pdopt或.pdparams。
# Global.save_inference_dir参数设置转换的模型将保存的地址。
-python3 tools/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./ch_lite/det_mv3_db/best_accuracy Global.save_inference_dir=./inference/det_db/
+python3 tools/export_model.py -c configs/det/det_mv3_db_v1.1.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v1.1_det_train/best_accuracy Global.save_inference_dir=./inference/det_db/
```
转inference模型时，使用的配置文件和训练时使用的配置文件相同。另外，还需要设置配置文件中的`Global.checkpoints`、`Global.save_inference_dir`参数。
其中`Global.checkpoints`指向训练中保存的模型参数文件，`Global.save_inference_dir`是生成的inference模型要保存的目录。
@@ -61,7 +67,7 @@ inference/det_db/
下载超轻量中文识别模型：
```
-wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar && tar xf ./ch_lite/ch_rec_mv3_crnn.tar -C ./ch_lite/
+wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v1.1_rec_train.tar -C ./ch_lite/
```
识别模型转inference模型与检测的方式相同，如下：
@@ -71,7 +77,7 @@ wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar
# Global.checkpoints参数设置待转换的训练模型地址，不用添加文件后缀.pdmodel，.pdopt或.pdparams。
# Global.save_inference_dir参数设置转换的模型将保存的地址。
-python3 tools/export_model.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints=./ch_lite/rec_mv3_crnn/best_accuracy \
+python3 tools/export_model.py -c configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v1.1_rec_train/best_accuracy \
Global.save_inference_dir=./inference/rec_crnn/
```
@@ -84,6 +90,32 @@ python3 tools/export_model.py -c configs/rec/rec_chinese_lite_train.yml -o Globa
└─ params 识别inference模型的参数文件
```
+
+### 方向分类模型转inference模型
+
+下载方向分类模型：
+```
+wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v1.1_cls_train.tar -C ./ch_lite/
+```
+
+方向分类模型转inference模型与检测的方式相同，如下：
+```
+# -c后面设置训练算法的yml配置文件
+# -o配置可选参数
+# Global.checkpoints参数设置待转换的训练模型地址，不用添加文件后缀.pdmodel，.pdopt或.pdparams。
+# Global.save_inference_dir参数设置转换的模型将保存的地址。
+
+python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v1.1_cls_train/best_accuracy \
+ Global.save_inference_dir=./inference/cls/
+```
+
+转换成功后，在目录下有两个文件：
+```
+/inference/cls/
+ └─ model 识别inference模型的program文件
+ └─ params 识别inference模型的参数文件
+```
+
## 二、文本检测模型推理
@@ -266,26 +298,83 @@ Predicts of ./doc/imgs_words_en/word_336.png:['super', 0.9999555]
self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
dict_character = list(self.character_str)
```
+
+### 4. 基于SRN损失的识别模型推理
+
+基于SRN损失的识别模型，需要额外设置识别算法参数 --rec_algorithm="SRN"。 同时需要保证预测shape与训练时一致，如： --rec_image_shape="1, 64, 256"
+
+```
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" \
+ --rec_model_dir="./inference/srn/" \
+ --rec_image_shape="1, 64, 256" \
+ --rec_char_type="en" \
+ --rec_algorithm="SRN"
+```
-### 4. 自定义文本识别字典的推理
+### 5. 自定义文本识别字典的推理
如果训练时修改了文本的字典，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径
```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_type="en" --rec_char_dict_path="your text dict path"
```
-
-## 四、文本检测、识别串联推理
+
+### 6. 多语言模型的推理
+如果您需要预测的是其他语言模型，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径, 同时为了得到正确的可视化结果，
+需要通过 `--vis_font_path` 指定可视化的字体路径，`doc/` 路径下有默认提供的小语种字体，例如韩文识别：
+
+```
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/korean.ttf"
+```
+
+
+执行命令后，上图的预测结果为：
+``` text
+2020-09-19 16:15:05,076-INFO: index: [205 206 38 39]
+2020-09-19 16:15:05,077-INFO: word : 바탕으로
+2020-09-19 16:15:05,077-INFO: score: 0.9171358942985535
+```
+
+
+## 四、方向分类模型推理
+
+下面将介绍方向分类模型推理。
+
+
+### 1. 方向分类模型推理
+
+方向分类模型推理，可以执行如下命令：
+
+```
+python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --cls_model_dir="./inference/cls/"
+```
+
+
+
+执行命令后，上面图像的预测结果（分类的方向和得分）会打印到屏幕上，示例如下：
+
+Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999963]
+
+
+## 五、文本检测、方向分类和文字识别串联推理
### 1. 超轻量中文OCR模型推理
-在执行预测时，需要通过参数image_dir指定单张图像或者图像集合的路径、参数det_model_dir指定检测inference模型的路径和参数rec_model_dir指定识别inference模型的路径。可视化识别结果默认保存到 ./inference_results 文件夹里面。
+在执行预测时，需要通过参数`image_dir`指定单张图像或者图像集合的路径、参数`det_model_dir`,`cls_model_dir`和`rec_model_dir`分别指定检测，方向分类和识别的inference模型路径。参数`use_angle_cls`用于控制是否启用方向分类模型。可视化识别结果默认保存到 ./inference_results 文件夹里面。
```
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/"
+# 使用方向分类器
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=true
+
+# 不使用方向分类器
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false
```
+
+
+
+
执行命令后，识别结果图像如下：
diff --git a/doc/doc_ch/installation.md b/doc/doc_ch/installation.md
index 5bc14b897aacb3c71e26291cc2028a71e709ef21..d4b0a67f3cbdcb7d4f3efa6ea44ff881f7598a38 100644
--- a/doc/doc_ch/installation.md
+++ b/doc/doc_ch/installation.md
@@ -2,12 +2,12 @@
经测试PaddleOCR可在glibc 2.23上运行，您也可以测试其他glibc版本或安装glic 2.23
PaddleOCR 工作环境
-- PaddlePaddle 1.7+
+- PaddlePaddle 1.8+ ，推荐使用 PaddlePaddle 2.0.0.beta
- python3.7
- glibc 2.23
- cuDNN 7.6+ (GPU)
-建议使用我们提供的docker运行PaddleOCR，有关docker、nvidia-docker使用请参考[链接](https://docs.docker.com/get-started/)。
+建议使用我们提供的docker运行PaddleOCR，有关docker、nvidia-docker使用请参考[链接](https://www.runoob.com/docker/docker-tutorial.html/)。
*如您希望使用 mac 或 windows直接运行预测代码，可以从第2步开始执行。*
@@ -47,19 +47,16 @@ docker images
hub.baidubce.com/paddlepaddle/paddle latest-gpu-cuda9.0-cudnn7-dev f56310dcc829
```
-**2. 安装PaddlePaddle Fluid v1.7**
+**2. 安装PaddlePaddle Fluid v2.0**
```
pip3 install --upgrade pip
-如果您的机器安装的是CUDA9，请运行以下命令安装
-python3 -m pip install paddlepaddle-gpu==1.7.2.post97 -i https://pypi.tuna.tsinghua.edu.cn/simple
-
-如果您的机器安装的是CUDA10，请运行以下命令安装
-python3 -m pip install paddlepaddle-gpu==1.7.2.post107 -i https://pypi.tuna.tsinghua.edu.cn/simple
+如果您的机器安装的是CUDA9或CUDA10，请运行以下命令安装
+python3 -m pip install paddlepaddle-gpu==2.0.0b0 -i https://mirror.baidu.com/pypi/simple
如果您的机器是CPU，请运行以下命令安装
-python3 -m pip install paddlepaddle==1.7.2 -i https://pypi.tuna.tsinghua.edu.cn/simple
+python3 -m pip install paddlepaddle==2.0.0b0 -i https://mirror.baidu.com/pypi/simple
更多的版本需求，请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
```
diff --git a/doc/doc_ch/models_list.md b/doc/doc_ch/models_list.md
new file mode 100644
index 0000000000000000000000000000000000000000..c85def4efbc8abdd5eeadbd7a4641b5776694491
--- /dev/null
+++ b/doc/doc_ch/models_list.md
@@ -0,0 +1,71 @@
+## OCR模型列表（V1.1，9月22日更新）
+
+- [一、文本检测模型](#文本检测模型)
+- [二、文本识别模型](#文本识别模型)
+ - [1. 中文识别模型](#中文识别模型)
+ - [2. 英文识别模型](#英文识别模型)
+ - [3. 多语言识别模型](#多语言识别模型)
+- [三、文本方向分类模型](#文本方向分类模型)
+
+PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训练模型`、`slim模型`，模型区别说明如下：
+
+|模型类型|模型格式|简介|
+|-|-|-|
+|推理模型|model、params|用于python预测引擎推理，[详情](./inference.md)|
+|训练模型、预训练模型|\*.pdmodel、\*.pdopt、\*.pdparams|训练过程中保存的checkpoints模型，保存的是模型的参数，多用于模型指标评估和恢复训练|
+|slim模型|\*.nb|用于lite部署|
+
+
+
+### 一、文本检测模型
+|模型名称|模型简介|配置文件|推理模型大小|下载地址|
+|-|-|-|-|-|
+|ch_ppocr_mobile_slim_v1.1_det|slim裁剪版超轻量模型，支持中英文、多语种文本检测|[det_mv3_db_v1.1.yml](../../configs/det/det_mv3_db_v1.1.yml)|1.4M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_infer.tar) / [slim模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_det_prune_opt.nb)|
+|ch_ppocr_mobile_v1.1_det|原始超轻量模型，支持中英文、多语种文本检测|[det_mv3_db_v1.1.yml](../../configs/det/det_mv3_db_v1.1.yml)|2.6M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar)|
+|ch_ppocr_server_v1.1_det|通用模型，支持中英文、多语种文本检测，比超轻量模型更大，但效果更好|[det_r18_vd_db_v1.1.yml](../../configs/det/det_r18_vd_db_v1.1.yml)|47.2M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_train.tar)|
+
+
+
+### 二、文本识别模型
+
+
+#### 1. 中文识别模型
+|模型名称|模型简介|配置文件|推理模型大小|下载地址|
+|-|-|-|-|-|
+|ch_ppocr_mobile_slim_v1.1_rec|slim裁剪量化版超轻量模型，支持中英文、数字识别|[rec_chinese_lite_train_v1.1.yml](../../configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml)|1.6M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_infer.tar) / [slim模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_rec_quant_opt.nb) |
+|ch_ppocr_mobile_v1.1_rec|原始超轻量模型，支持中英文、数字识别|[rec_chinese_lite_train_v1.1.yml](../../configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml)|4.6M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_train.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_pre.tar) |
+|ch_ppocr_server_v1.1_rec|通用模型，支持中英文、数字识别|[rec_chinese_common_train_v1.1.yml](../../configs/rec/ch_ppocr_v1.1/rec_chinese_common_train_v1.1.yml)|105M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_train.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_pre.tar) |
+
+**说明：** `训练模型`是基于预训练模型在真实数据与竖排合成文本数据上finetune得到的模型，在真实应用场景中有着更好的表现，`预训练模型`则是直接基于全量真实数据与合成数据训练得到，更适合用于在自己的数据集上finetune。
+
+
+#### 2. 英文识别模型
+|模型名称|模型简介|配置文件|推理模型大小|下载地址|
+|-|-|-|-|-|
+|en_ppocr_mobile_slim_v1.1_rec|slim裁剪量化版超轻量模型，支持英文、数字识别|[rec_en_lite_train.yml](../../configs/rec/multi_languages/rec_en_lite_train.yml)|0.9M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/en/en_ppocr_mobile_v1.1_rec_quant_infer.tar) / [slim模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/en/en_ppocr_mobile_v1.1_rec_quant_opt.nb) |
+|en_ppocr_mobile_v1.1_rec|原始超轻量模型，支持英文、数字识别|[rec_en_lite_train.yml](../../configs/rec/multi_languages/rec_en_lite_train.yml)|2.0M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/en/en_ppocr_mobile_v1.1_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/en/en_ppocr_mobile_v1.1_rec_train.tar) |
+
+
+#### 3. 多语言识别模型（更多语言持续更新中...）
+|模型名称|模型简介|配置文件|推理模型大小|下载地址|
+|-|-|-|-|-|
+| french_ppocr_mobile_v1.1_rec |法文识别|[rec_french_lite_train.yml](../../configs/rec/multi_languages/rec_french_lite_train.yml)|2.1M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/fr/french_ppocr_mobile_v1.1_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/fr/french_ppocr_mobile_v1.1_rec_train.tar) |
+| german_ppocr_mobile_v1.1_rec |德文识别|[rec_ger_lite_train.yml](../../configs/rec/multi_languages/rec_ger_lite_train.yml)|2.1M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/ge/german_ppocr_mobile_v1.1_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/ge/german_ppocr_mobile_v1.1_rec_train.tar) |
+| korean_ppocr_mobile_v1.1_rec |韩文识别|[rec_korean_lite_train.yml](../../configs/rec/multi_languages/rec_korean_lite_train.yml)|3.4M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/kr/korean_ppocr_mobile_v1.1_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/kr/korean_ppocr_mobile_v1.1_rec_train.tar) |
+| japan_ppocr_mobile_v1.1_rec |日文识别|[rec_japan_lite_train.yml](../../configs/rec/multi_languages/rec_japan_lite_train.yml)|3.7M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/jp/japan_ppocr_mobile_v1.1_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/jp/japan_ppocr_mobile_v1.1_rec_train.tar) |
+
+
+
+### 三、文本方向分类模型
+|模型名称|模型简介|配置文件|推理模型大小|下载地址|
+|-|-|-|-|-|
+|ch_ppocr_mobile_v1.1_cls_quant|slim量化版模型|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)|0.5M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_quant_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_quant_train.tar) / [slim模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_cls_quant_opt.nb) |
+|ch_ppocr_mobile_v1.1_cls|原始模型|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)|850kb|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar) |
+
+
+## OCR模型列表（V1.0，7月16日更新）
+
+|模型名称|模型简介|检测模型地址|识别模型地址|支持空格的识别模型地址|
+|-|-|-|-|-|
+|chinese_db_crnn_mobile|8.6M超轻量级中文OCR模型|[推理模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar) |[推理模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar) |[推理模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar)
+|chinese_db_crnn_server|通用中文OCR模型|[推理模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar) |[推理模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar) |[推理模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)
diff --git a/doc/doc_ch/quickstart.md b/doc/doc_ch/quickstart.md
index 701b50ed36fc69a6285550e6f53f6f3a09a1a63d..97c3d41dae337195f0ac1517a4c276a886e7cc94 100644
--- a/doc/doc_ch/quickstart.md
+++ b/doc/doc_ch/quickstart.md
@@ -9,12 +9,15 @@
## 2.inference模型下载
-|模型名称|模型简介|检测模型地址|识别模型地址|支持空格的识别模型地址|
-|-|-|-|-|-|
-|chinese_db_crnn_mobile|超轻量级中文OCR模型|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar)
-|chinese_db_crnn_server|通用中文OCR模型|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)
+* 移动端和服务器端的检测与识别模型如下，更多模型下载（包括多语言），可以参考[PP-OCR v1.1 系列模型下载](../doc_ch/models_list.md)
-*windows 环境下如果没有安装wget,下载模型时可将链接复制到浏览器中下载，并解压放置在相应目录下*
+| 模型简介 | 模型名称 |推荐场景 | 检测模型 | 方向分类器 | 识别模型 |
+| ------------ | --------------- | ----------------|---- | ---------- | -------- |
+| 中英文超轻量OCR模型（8.1M） | ch_ppocr_mobile_v1.1_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar)|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_pre.tar) |
+| 中英文通用OCR模型（155.1M） |ch_ppocr_server_v1.1_xx|服务器端 |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_pre.tar) |
+
+
+* windows 环境下如果没有安装wget,下载模型时可将链接复制到浏览器中下载，并解压放置在相应目录下
复制上表中的检测和识别的`inference模型`下载地址，并解压
@@ -24,6 +27,8 @@ mkdir inference && cd inference
wget {url/of/detection/inference_model} && tar xf {name/of/detection/inference_model/package}
# 下载识别模型并解压
wget {url/of/recognition/inference_model} && tar xf {name/of/recognition/inference_model/package}
+# 下载方向分类器模型并解压
+wget {url/of/classification/inference_model} && tar xf {name/of/classification/inference_model/package}
cd ..
```
@@ -32,9 +37,11 @@ cd ..
```
mkdir inference && cd inference
# 下载超轻量级中文OCR模型的检测模型并解压
-wget https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar && tar xf ch_det_mv3_db_infer.tar
+wget https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar && tar xf ch_ppocr_mobile_v1.1_det_infer.tar
# 下载超轻量级中文OCR模型的识别模型并解压
-wget https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar && tar xf ch_rec_mv3_crnn_infer.tar
+wget https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar && tar xf ch_ppocr_mobile_v1.1_rec_infer.tar
+# 下载超轻量级中文OCR模型的文本方向分类器模型并解压
+wget https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar && tar xf ch_ppocr_mobile_v1.1_cls_infer.tar
cd ..
```
@@ -42,10 +49,13 @@ cd ..
```
|-inference
- |-ch_rec_mv3_crnn
+ |-ch_ppocr_mobile_v1.1_det_infer
+ |- model
+ |- params
+ |-ch_ppocr_mobile_v1.1_rec_infer
|- model
|- params
- |-ch_det_mv3_db
+ |-ch_ppocr_mobile-v1.1_cls_infer
|- model
|- params
...
@@ -53,42 +63,37 @@ cd ..
## 3.单张图像或者图像集合预测
-以下代码实现了文本检测、识别串联推理，在执行预测时，需要通过参数image_dir指定单张图像或者图像集合的路径、参数det_model_dir指定检测inference模型的路径和参数rec_model_dir指定识别inference模型的路径。可视化识别结果默认保存到 ./inference_results 文件夹里面。
+以下代码实现了文本检测、识别串联推理，在执行预测时，需要通过参数image_dir指定单张图像或者图像集合的路径、参数`det_model_dir`指定检测inference模型的路径、参数`rec_model_dir`指定识别inference模型的路径、参数`use_angle_cls`指定是否使用方向分类器、参数`cls_model_dir`指定方向分类器inference模型的路径、参数`use_space_char`指定是否预测空格字符。可视化识别结果默认保存到`./inference_results`文件夹里面。
```bash
# 预测image_dir指定的单张图像
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_det_mv3_db/" --rec_model_dir="./inference/ch_rec_mv3_crnn/"
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_mobile_v1.1_det_infer/" --rec_model_dir="./inference/ch_ppocr_mobile_v1.1_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v1.1_cls_infer/" --use_angle_cls=True --use_space_char=True
# 预测image_dir指定的图像集合
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/" --det_model_dir="./inference/ch_det_mv3_db/" --rec_model_dir="./inference/ch_rec_mv3_crnn/"
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/" --det_model_dir="./inference/ch_ppocr_mobile_v1.1_det_infer/" --rec_model_dir="./inference/ch_ppocr_mobile_v1.1_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v1.1_cls_infer/" --use_angle_cls=True --use_space_char=True
# 如果想使用CPU进行预测，需设置use_gpu参数为False
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_det_mv3_db/" --rec_model_dir="./inference/ch_rec_mv3_crnn/" --use_gpu=False
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_mobile_v1.1_det_infer/" --rec_model_dir="./inference/ch_ppocr_mobile_v1.1_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v1.1_cls_infer/" --use_angle_cls=True --use_space_char=True --use_gpu=False
```
- 通用中文OCR模型
请按照上述步骤下载相应的模型，并且更新相关的参数，示例如下：
-```
+
+```bash
# 预测image_dir指定的单张图像
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_det_r50_vd_db/" --rec_model_dir="./inference/ch_rec_r34_vd_crnn/"
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_server_v1.1_det_infer/" --rec_model_dir="./inference/ch_ppocr_server_v1.1_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v1.1_cls_infer/" --use_angle_cls=True --use_space_char=True
```
-- 支持空格的通用中文OCR模型
+* 注意：
+ - 如果希望使用不支持空格的识别模型，在预测的时候需要注意：请将代码更新到最新版本，并添加参数 `--use_space_char=False`。
+ - 如果不希望使用方向分类器，在预测的时候需要注意：请将代码更新到最新版本，并添加参数 `--use_angle_cls=False`。
-请按照上述步骤下载相应的模型，并且更新相关的参数，示例如下：
-
-*注意：请将代码更新到最新版本，并添加参数 `--use_space_char=True` *
-
-```
-# 预测image_dir指定的单张图像
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_12.jpg" --det_model_dir="./inference/ch_det_r50_vd_db/" --rec_model_dir="./inference/ch_rec_r34_vd_crnn_enhance/" --use_space_char=True
-```
更多的文本检测、识别串联推理使用方式请参考文档教程中[基于Python预测引擎推理](./inference.md)。
此外，文档教程中也提供了中文OCR模型的其他预测部署方式：
- [基于C++预测引擎推理](../../deploy/cpp_infer/readme.md)
-- [服务部署](./serving.md)
+- [服务部署](../../deploy/pdserving/readme.md)
- [端侧部署](../../deploy/lite/readme.md)
diff --git a/doc/doc_ch/recognition.md b/doc/doc_ch/recognition.md
index c554b9f11c96744ae928aaf9992606a364680557..71be1e89dc561630315337ef11c52289e0756c00 100644
--- a/doc/doc_ch/recognition.md
+++ b/doc/doc_ch/recognition.md
@@ -1,5 +1,24 @@
## 文字识别
+
+- [一、数据准备](#数据准备)
+ - [数据下载](#数据下载)
+ - [自定义数据集](#自定义数据集)
+ - [字典](#字典)
+ - [支持空格](#支持空格)
+
+- [二、启动训练](#文本检测模型推理)
+ - [1. 数据增强](#数据增强)
+ - [2. 训练](#训练)
+ - [3. 小语种](#小语种)
+
+- [三、评估](#评估)
+
+- [四、预测](#预测)
+ - [1. 训练引擎预测](#训练引擎预测)
+
+
+
### 数据准备
@@ -13,16 +32,18 @@ PaddleOCR 支持两种数据格式: `lmdb` 用于训练公开数据，调试算
ln -sf /train_data/dataset
```
-
+
* 数据下载
若您本地没有数据集，可以在官网下载 [icdar2015](http://rrc.cvc.uab.es/?ch=4&com=downloads) 数据，用于快速验证。也可以参考[DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here)，下载 benchmark 所需的lmdb格式数据集。
如果希望复现SRN的论文指标，需要下载离线[增广数据](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA),提取码: y3ry。增广数据是由MJSynth和SynthText做旋转和扰动得到的。数据下载完成后请解压到 {your_path}/PaddleOCR/train_data/data_lmdb_release/training/ 路径下。
-* 使用自己数据集：
+
+* 使用自己数据集
若您希望使用自己的数据进行训练，请参考下文组织您的数据。
+
- 训练集
首先请将训练图片放入同一个文件夹（train_images），并用一个txt文件（rec_gt_train.txt）记录图片路径和标签。
@@ -44,6 +65,13 @@ wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_t
wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_test.txt
```
+PaddleOCR 也提供了数据格式转换脚本，可以将官网 label 转换支持的数据格式。 数据转换工具在 `train_data/gen_label.py`, 这里以训练集为例：
+
+```
+# 将官网下载的标签文件转换为 rec_gt_label.txt
+python gen_label.py --mode="rec" --input_path="{path/of/origin/label}" --output_label="rec_gt_label.txt"
+```
+
最终训练集应有如下文件结构：
```
|-train_data
@@ -70,7 +98,7 @@ wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_t
|- word_003.jpg
| ...
```
-
+
- 字典
最后需要提供一个字典（{word_dict_name}.txt），使模型在训练时，可以将所有出现的字符映射为字典的索引。
@@ -89,21 +117,36 @@ n
word_dict.txt 每行有一个单字，将字符与数字索引映射在一起，“and” 将被映射成 [2 5 1]
`ppocr/utils/ppocr_keys_v1.txt` 是一个包含6623个字符的中文字典，
+
`ppocr/utils/ic15_dict.txt` 是一个包含36个字符的英文字典，
+
+`ppocr/utils/dict/french_dict.txt` 是一个包含118个字符的法文字典
+
+`ppocr/utils/dict/japan_dict.txt` 是一个包含4399个字符的法文字典
+
+`ppocr/utils/dict/korean_dict.txt` 是一个包含3636个字符的法文字典
+
+`ppocr/utils/dict/german_dict.txt` 是一个包含131个字符的法文字典
+
+
您可以按需使用。
+目前的多语言模型仍处在demo阶段，会持续优化模型并补充语种，**非常欢迎您为我们提供其他语言的字典和字体**，
+如您愿意可将字典文件提交至 [dict](../../ppocr/utils/dict) 将语料文件提交至[corpus](../../ppocr/utils/corpus)，我们会在Repo中感谢您。
+
- 自定义字典
如需自定义dic文件，请在 `configs/rec/rec_icdar15_train.yml` 中添加 `character_dict_path` 字段, 指向您的字典路径。
并将 `character_type` 设置为 `ch`。
+
- 添加空格类别
如果希望支持识别"空格"类别, 请将yml文件中的 `use_space_char` 字段设置为 `true`。
**注意：`use_space_char` 仅在 `character_type=ch` 时生效**
-
+
### 启动训练
PaddleOCR提供了训练脚本、评估脚本和预测脚本，本节将以 CRNN 识别模型为例：
@@ -124,14 +167,12 @@ tar -xf rec_mv3_none_bilstm_ctc.tar && rm -rf rec_mv3_none_bilstm_ctc.tar
*如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false*
```
-# 设置PYTHONPATH路径
-export PYTHONPATH=$PYTHONPATH:.
# GPU训练 支持单卡，多卡训练，通过CUDA_VISIBLE_DEVICES指定卡号
export CUDA_VISIBLE_DEVICES=0,1,2,3
-# 训练icdar15英文数据
-python3 tools/train.py -c configs/rec/rec_icdar15_train.yml
+# 训练icdar15英文数据 并将训练日志保存为 tain_rec.log
+python3 tools/train.py -c configs/rec/rec_icdar15_train.yml 2>&1 | tee train_rec.log
```
-
+
- 数据增强
PaddleOCR提供了多种数据增强方式，如果您希望在训练时加入扰动，请在配置文件中设置 `distort: true`。
@@ -140,8 +181,9 @@ PaddleOCR提供了多种数据增强方式，如果您希望在训练时加入
训练过程中每种扰动方式以50%的概率被选择，具体代码实现请参考：[img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py)
-*由于OpenCV的兼容性问题，扰动操作暂时只支持GPU*
+*由于OpenCV的兼容性问题，扰动操作暂时只支持Linux*
+
- 训练
PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/rec_icdar15_train.yml` 中修改 `eval_batch_step` 设置评估频率，默认每500个iter评估一次。评估过程中默认将最佳acc模型，保存为 `output/rec_CRNN/best_accuracy` 。
@@ -153,7 +195,10 @@ PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/rec_icdar15_t
| 配置文件 | 算法名称 | backbone | trans | seq | pred |
| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: |
+| [rec_chinese_lite_train_v1.1.yml](../../configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml) | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc |
+| [rec_chinese_common_train_v1.1.yml](../../configs/rec/ch_ppocr_v1.1/rec_chinese_common_train_v1.1.yml) | CRNN | ResNet34_vd | None | BiLSTM | ctc |
| rec_chinese_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc |
+| rec_chinese_common_train.yml | CRNN | ResNet34_vd | None | BiLSTM | ctc |
| rec_icdar15_train.yml | CRNN | Mobilenet_v3 large 0.5 | None | BiLSTM | ctc |
| rec_mv3_none_bilstm_ctc.yml | CRNN | Mobilenet_v3 large 0.5 | None | BiLSTM | ctc |
| rec_mv3_none_none_ctc.yml | Rosetta | Mobilenet_v3 large 0.5 | None | None | ctc |
@@ -165,7 +210,7 @@ PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/rec_icdar15_t
| rec_r34_vd_tps_bilstm_ctc.yml | STARNet | Resnet34_vd | tps | BiLSTM | ctc |
| rec_r50fpn_vd_none_srn.yml | SRN | Resnet50_fpn_vd | None | rnn | srn |
-训练中文数据，推荐使用`rec_chinese_lite_train.yml`，如您希望尝试其他算法在中文数据集上的效果，请参考下列说明修改配置文件：
+训练中文数据，推荐使用[rec_chinese_lite_train_v1.1.yml](../../configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml)，如您希望尝试其他算法在中文数据集上的效果，请参考下列说明修改配置文件：
以 `rec_mv3_none_none_ctc.yml` 为例：
```
@@ -201,8 +246,54 @@ Optimizer:
```
**注意，预测/评估时的配置文件请务必与训练一致。**
+
+- 小语种
+
+PaddleOCR也提供了多语言的， `configs/rec/multi_languages` 路径下的提供了多语言的配置文件，目前PaddleOCR支持的多语言算法有：
+| 配置文件 | 算法名称 | backbone | trans | seq | pred | language |
+| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: | :-----: |
+| rec_en_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 英语 |
+| rec_french_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 法语 |
+| rec_ger_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 德语 |
+| rec_japan_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 日语 |
+| rec_korean_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 韩语 |
+
+多语言模型训练方式与中文模型一致，训练数据集均为100w的合成数据，少量的字体可以在 [百度网盘](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA) 上下载，提取码：frgi。
+
+如您希望在现有模型效果的基础上调优，请参考下列说明修改配置文件：
+
+以 `rec_french_lite_train` 为例：
+```
+Global:
+ ...
+ # 添加自定义字典，如修改字典请将路径指向新字典
+ character_dict_path: ./ppocr/utils/dict/french_dict.txt
+ # 训练时添加数据增强
+ distort: true
+ # 识别空格
+ use_space_char: true
+ ...
+ # 修改reader类型
+ reader_yml: ./configs/rec/multi_languages/rec_french_reader.yml
+ ...
+...
+```
+
+同时需要修改数据读取文件 `rec_french_reader.yml`：
+
+```
+TrainReader:
+ ...
+ # 修改训练数据存放的目录名
+ img_set_dir: ./train_data
+ # 修改 label 文件名称
+ label_file_path: ./train_data/french_train.txt
+
+...
+```
+
### 评估
评估数据集可以通过 `configs/rec/rec_icdar15_reader.yml` 修改EvalReader中的 `label_file_path` 设置。
@@ -214,8 +305,10 @@ export CUDA_VISIBLE_DEVICES=0
python3 tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
```
+
### 预测
+
* 训练引擎的预测
使用 PaddleOCR 训练好的模型，可以通过以下脚本进行快速预测。
@@ -239,12 +332,12 @@ infer_img: doc/imgs_words/en/word_1.png
word : joint
```
-预测使用的配置文件必须与训练一致，如您通过 `python3 tools/train.py -c configs/rec/rec_chinese_lite_train.yml` 完成了中文模型的训练，
+预测使用的配置文件必须与训练一致，如您通过 `python3 tools/train.py -c configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml` 完成了中文模型的训练，
您可以使用如下命令进行中文模型预测。
```
# 预测中文结果
-python3 tools/infer_rec.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/ch/word_1.jpg
+python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/ch/word_1.jpg
```
预测图片：
diff --git a/doc/doc_ch/serving.md b/doc/doc_ch/serving.md
deleted file mode 100644
index 892745671e639ccd19bec2bc4c789d48d43dfad9..0000000000000000000000000000000000000000
--- a/doc/doc_ch/serving.md
+++ /dev/null
@@ -1,176 +0,0 @@
-# 服务部署
-
-PaddleOCR提供2种服务部署方式：
-- 基于HubServing的部署：已集成到PaddleOCR中（[code](https://github.com/PaddlePaddle/PaddleOCR/tree/develop/deploy/hubserving)），按照本教程使用；
-- 基于PaddleServing的部署：详见PaddleServing官网[demo](https://github.com/PaddlePaddle/Serving/tree/develop/python/examples/ocr)，后续也将集成到PaddleOCR。
-
-服务部署目录下包括检测、识别、2阶段串联三种服务包，根据需求选择相应的服务包进行安装和启动。目录如下：
-```
-deploy/hubserving/
- └─ ocr_det 检测模块服务包
- └─ ocr_rec 识别模块服务包
- └─ ocr_system 检测+识别串联服务包
-```
-
-每个服务包下包含3个文件。以2阶段串联服务包为例，目录如下：
-```
-deploy/hubserving/ocr_system/
- └─ __init__.py 空文件，必选
- └─ config.json 配置文件，可选，使用配置启动服务时作为参数传入
- └─ module.py 主模块，必选，包含服务的完整逻辑
- └─ params.py 参数文件，必选，包含模型路径、前后处理参数等参数
-```
-
-## 快速启动服务
-以下步骤以检测+识别2阶段串联服务为例，如果只需要检测服务或识别服务，替换相应文件路径即可。
-### 1. 准备环境
-```shell
-# 安装paddlehub
-pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
-
-# 在Linux下设置环境变量
-export PYTHONPATH=.
-# 在Windows下设置环境变量
-SET PYTHONPATH=.
-```
-
-### 2. 安装服务模块
-PaddleOCR提供3种服务模块，根据需要安装所需模块。
-
-* 在Linux环境下，安装示例如下：
-```shell
-# 安装检测服务模块：
-hub install deploy/hubserving/ocr_det/
-
-# 或，安装识别服务模块：
-hub install deploy/hubserving/ocr_rec/
-
-# 或，安装检测+识别串联服务模块：
-hub install deploy/hubserving/ocr_system/
-```
-
-* 在Windows环境下(文件夹的分隔符为`\`)，安装示例如下：
-```shell
-# 安装检测服务模块：
-hub install deploy\hubserving\ocr_det\
-
-# 或，安装识别服务模块：
-hub install deploy\hubserving\ocr_rec\
-
-# 或，安装检测+识别串联服务模块：
-hub install deploy\hubserving\ocr_system\
-```
-
-### 3. 启动服务
-#### 方式1. 命令行命令启动（仅支持CPU）
-**启动命令：**
-```shell
-$ hub serving start --modules [Module1==Version1, Module2==Version2, ...] \
- --port XXXX \
- --use_multiprocess \
- --workers \
-```
-
-**参数：**
-
-|参数|用途|
-|-|-|
-|--modules/-m|PaddleHub Serving预安装模型，以多个Module==Version键值对的形式列出
*`当不指定Version时，默认选择最新版本`*|
-|--port/-p|服务端口，默认为8866|
-|--use_multiprocess|是否启用并发方式，默认为单进程方式，推荐多核CPU机器使用此方式
*`Windows操作系统只支持单进程方式`*|
-|--workers|在并发方式下指定的并发任务数，默认为`2*cpu_count-1`，其中`cpu_count`为CPU核数|
-
-如启动串联服务： ```hub serving start -m ocr_system```
-
-这样就完成了一个服务化API的部署，使用默认端口号8866。
-
-#### 方式2. 配置文件启动（支持CPU、GPU）
-**启动命令：**
-```hub serving start -c config.json```
-
-其中，`config.json`格式如下：
-```python
-{
- "modules_info": {
- "ocr_system": {
- "init_args": {
- "version": "1.0.0",
- "use_gpu": true
- },
- "predict_args": {
- }
- }
- },
- "port": 8868,
- "use_multiprocess": false,
- "workers": 2
-}
-```
-
-- `init_args`中的可配参数与`module.py`中的`_initialize`函数接口一致。其中，**当`use_gpu`为`true`时，表示使用GPU启动服务**。
-- `predict_args`中的可配参数与`module.py`中的`predict`函数接口一致。
-
-**注意:**
-- 使用配置文件启动服务时，其他参数会被忽略。
-- 如果使用GPU预测(即，`use_gpu`置为`true`)，则需要在启动服务之前，设置CUDA_VISIBLE_DEVICES环境变量，如：```export CUDA_VISIBLE_DEVICES=0```，否则不用设置。
-- **`use_gpu`不可与`use_multiprocess`同时为`true`**。
-
-如，使用GPU 3号卡启动串联服务：
-```shell
-export CUDA_VISIBLE_DEVICES=3
-hub serving start -c deploy/hubserving/ocr_system/config.json
-```
-
-## 发送预测请求
-配置好服务端，可使用以下命令发送预测请求，获取预测结果:
-
-```python tools/test_hubserving.py server_url image_path```
-
-需要给脚本传递2个参数：
-- **server_url**：服务地址，格式为
-`http://[ip_address]:[port]/predict/[module_name]`
-例如，如果使用配置文件启动检测、识别、检测+识别2阶段服务，那么发送请求的url将分别是：
-`http://127.0.0.1:8866/predict/ocr_det`
-`http://127.0.0.1:8867/predict/ocr_rec`
-`http://127.0.0.1:8868/predict/ocr_system`
-- **image_path**：测试图像路径，可以是单张图片路径，也可以是图像集合目录路径
-
-访问示例：
-```python tools/test_hubserving.py http://127.0.0.1:8868/predict/ocr_system ./doc/imgs/```
-
-## 返回结果格式说明
-返回结果为列表（list），列表中的每一项为词典（dict），词典一共可能包含3种字段，信息如下：
-
-|字段名称|数据类型|意义|
-|-|-|-|
-|text|str|文本内容|
-|confidence|float| 文本识别置信度|
-|text_region|list|文本位置坐标|
-
-不同模块返回的字段不同，如，文本识别服务模块返回结果不含`text_region`字段，具体信息如下：
-
-|字段名/模块名|ocr_det|ocr_rec|ocr_system|
-|-|-|-|-|
-|text||✔|✔|
-|confidence||✔|✔|
-|text_region|✔||✔|
-
-**说明：** 如果需要增加、删除、修改返回字段，可在相应模块的`module.py`文件中进行修改，完整流程参考下一节自定义修改服务模块。
-
-## 自定义修改服务模块
-如果需要修改服务逻辑，你一般需要操作以下步骤（以修改`ocr_system`为例）：
-
-- 1、 停止服务
-```hub serving stop --port/-p XXXX```
-
-- 2、 到相应的`module.py`和`params.py`等文件中根据实际需求修改代码。
-例如，如果需要替换部署服务所用模型，则需要到`params.py`中修改模型路径参数`det_model_dir`和`rec_model_dir`，当然，同时可能还需要修改其他相关参数，请根据实际情况修改调试。 建议修改后先直接运行`module.py`调试，能正确运行预测后再启动服务测试。
-
-- 3、 卸载旧服务包
-```hub uninstall ocr_system```
-
-- 4、 安装修改后的新服务包
-```hub install deploy/hubserving/ocr_system/```
-
-- 5、重新启动服务
-```hub serving start -m ocr_system```
diff --git a/doc/doc_ch/serving_inference.md b/doc/doc_ch/serving_inference.md
new file mode 100644
index 0000000000000000000000000000000000000000..7a53628e2f93d4d0ec944ec18ec5f06452698512
--- /dev/null
+++ b/doc/doc_ch/serving_inference.md
@@ -0,0 +1,239 @@
+# 使用Paddle Serving预测推理
+
+阅读本文档之前，请先阅读文档 [基于Python预测引擎推理](./inference.md)
+
+同本地执行预测一样，我们需要保存一份可以用于Paddle Serving的模型。
+
+接下来首先介绍如何将训练的模型转换成Paddle Serving模型，然后将依次介绍文本检测、文本识别以及两者串联基于预测引擎推理。
+
+### 一、 准备环境
+我们先安装Paddle Serving相关组件
+我们推荐用户使用GPU来做Paddle Serving的OCR服务部署
+
+**CUDA版本：9.X/10.X**
+
+**CUDNN版本：7.X**
+
+**操作系统版本：Linux/Windows**
+
+**Python版本： 2.7/3.5/3.6/3.7**
+
+**Python操作指南：**
+
+目前Serving用于OCR的部分功能还在测试当中，因此在这里我们给出[Servnig latest package](https://github.com/PaddlePaddle/Serving/blob/develop/doc/LATEST_PACKAGES.md)
+大家根据自己的环境选择需要安装的whl包即可，例如以Python 3.5为例，执行下列命令
+```
+#CPU/GPU版本选择一个
+#GPU版本服务端
+#CUDA 9
+python -m pip install -U https://paddle-serving.bj.bcebos.com/whl/paddle_serving_server_gpu-0.0.0.post9-py3-none-any.whl
+#CUDA 10
+python -m pip install -U https://paddle-serving.bj.bcebos.com/whl/paddle_serving_server_gpu-0.0.0.post10-py3-none-any.whl
+#CPU版本服务端
+python -m pip install -U https://paddle-serving.bj.bcebos.com/whl/paddle_serving_server-0.0.0-py3-none-any.whl
+#客户端和App包使用以下链接（CPU，GPU通用）
+python -m pip install -U https://paddle-serving.bj.bcebos.com/whl/paddle_serving_client-0.0.0-cp36-none-any.whl https://paddle-serving.bj.bcebos.com/whl/paddle_serving_app-0.0.0-py3-none-any.whl
+```
+
+## 二、训练模型转Serving模型
+
+在前序文档 [基于Python预测引擎推理](./inference.md) 中，我们提供了如何把训练的checkpoint转换成Paddle模型。Paddle模型通常由一个文件夹构成，内含模型结构描述文件`model`和模型参数文件`params`。Serving模型由两个文件夹构成，用于存放客户端和服务端的配置。
+
+我们以`ch_rec_r34_vd_crnn`模型作为例子，下载链接在：
+
+```
+wget --no-check-certificate https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar
+tar xf ch_rec_r34_vd_crnn_infer.tar
+```
+因此我们按照Serving模型转换教程，运行下列python文件。
+```
+python tools/inference_to_serving.py --model_dir ch_rec_r34_vd_crnn
+```
+最终会在`serving_client_dir`和`serving_server_dir`生成客户端和服务端的模型配置。其中`serving_server_dir`和`serving_client_dir`的名字可以自定义。最终文件结构如下
+
+```
+/ch_rec_r34_vd_crnn/
+├── serving_client_dir # 客户端配置文件夹
+└── serving_server_dir # 服务端配置文件夹
+```
+
+## 三、文本检测模型Serving推理
+
+启动服务可以根据实际需求选择启动`标准版`或者`快速版`，两种方式的对比如下表：
+
+|版本|特点|适用场景|
+|-|-|-|
+|标准版|稳定性高，分布式部署|适用于吞吐量大，需要跨机房部署的情况|
+|快速版|部署方便，预测速度快|适用于对预测速度要求高，迭代速度快的场景，Windows用户只能选择快速版|
+
+接下来的命令中，我们会指定快速版和标准版的命令。需要说明的是，标准版只能用Linux平台，快速版可以支持Linux/Windows。
+文本检测模型推理，默认使用DB模型的配置参数，识别默认为CRNN。
+
+配置文件在`params.py`中，我们贴出配置部分，如果需要做改动，也在这个文件内部进行修改。
+
+```
+def read_params():
+ cfg = Config()
+ #use gpu
+ cfg.use_gpu = False # 是否使用GPU
+ cfg.use_pdserving = True # 是否使用paddleserving，必须为True
+
+ #params for text detector
+ cfg.det_algorithm = "DB" # 检测算法， DB/EAST等
+ cfg.det_model_dir = "./det_mv_server/" # 检测算法模型路径
+ cfg.det_max_side_len = 960
+
+ #DB params
+ cfg.det_db_thresh =0.3
+ cfg.det_db_box_thresh =0.5
+ cfg.det_db_unclip_ratio =2.0
+
+ #EAST params
+ cfg.det_east_score_thresh = 0.8
+ cfg.det_east_cover_thresh = 0.1
+ cfg.det_east_nms_thresh = 0.2
+
+ #params for text recognizer
+ cfg.rec_algorithm = "CRNN" # 识别算法， CRNN/RARE等
+ cfg.rec_model_dir = "./ocr_rec_server/" # 识别算法模型路径
+
+ cfg.rec_image_shape = "3, 32, 320"
+ cfg.rec_char_type = 'ch'
+ cfg.rec_batch_num = 30
+ cfg.max_text_length = 25
+
+ cfg.rec_char_dict_path = "./ppocr_keys_v1.txt" # 识别算法字典文件
+ cfg.use_space_char = True
+
+ #params for text classifier
+ cfg.use_angle_cls = True # 是否启用分类算法
+ cfg.cls_model_dir = "./ocr_clas_server/" # 分类算法模型路径
+ cfg.cls_image_shape = "3, 48, 192"
+ cfg.label_list = ['0', '180']
+ cfg.cls_batch_num = 30
+ cfg.cls_thresh = 0.9
+
+ return cfg
+```
+与本地预测不同的是，Serving预测需要一个客户端和一个服务端，因此接下来的教程都是两行代码。
+
+在正式执行服务端启动命令之前，先export PYTHONPATH到工程主目录下。
+```
+export PYTHONPATH=$PWD:$PYTHONPATH
+cd deploy/pdserving
+```
+为了方便用户复现Demo程序，我们提供了Chinese and English ultra-lightweight OCR model (8.1M)版本的Serving模型
+```
+wget --no-check-certificate https://paddleocr.bj.bcebos.com/deploy/pdserving/ocr_pdserving_suite.tar.gz
+tar xf ocr_pdserving_suite.tar.gz
+```
+
+### 1. 超轻量中文检测模型推理
+
+超轻量中文检测模型推理，可以执行如下命令启动服务端：
+
+```
+#根据环境只需要启动其中一个就可以
+python det_rpc_server.py #标准版，Linux用户
+python det_local_server.py #快速版，Windows/Linux用户
+```
+
+客户端
+
+```
+python det_web_client.py
+```
+
+
+Serving的推测和本地预测不同点在于，客户端发送请求到服务端，服务端需要检测到文字框之后返回框的坐标，此处没有后处理的图片，只能看到坐标值。
+
+## 四、文本识别模型Serving推理
+
+下面将介绍超轻量中文识别模型推理、基于CTC损失的识别模型推理和基于Attention损失的识别模型推理。对于中文文本识别，建议优先选择基于CTC损失的识别模型，实践中也发现基于Attention损失的效果不如基于CTC损失的识别模型。此外，如果训练时修改了文本的字典，请参考下面的自定义文本识别字典的推理。
+
+### 1. 超轻量中文识别模型推理
+
+超轻量中文识别模型推理，可以执行如下命令启动服务端：
+需要注意params.py中的`--use_gpu`的值
+```
+#根据环境只需要启动其中一个就可以
+python rec_rpc_server.py #标准版，Linux用户
+python rec_local_server.py #快速版，Windows/Linux用户
+```
+如果需要使用CPU版本，还需增加 `--use_gpu False`。
+
+客户端
+
+```
+python rec_web_client.py
+```
+
+
+
+执行命令后，上面图像的预测结果（识别的文本和得分）会打印到屏幕上，示例如下：
+
+```
+{u'result': {u'score': [u'0.89547354'], u'pred_text': ['实力活力']}}
+```
+
+
+
+## 五、方向分类模型推理
+
+下面将介绍方向分类模型推理。
+
+
+
+### 1. 方向分类模型推理
+
+方向分类模型推理， 可以执行如下命令启动服务端：
+需要注意params.py中的`--use_gpu`的值
+```
+#根据环境只需要启动其中一个就可以
+python clas_rpc_server.py #标准版，Linux用户
+python clas_local_server.py #快速版，Windows/Linux用户
+```
+
+客户端
+
+```
+python rec_web_client.py
+```
+
+
+
+执行命令后，上面图像的预测结果（分类的方向和得分）会打印到屏幕上，示例如下：
+
+```
+{u'result': {u'direction': [u'0'], u'score': [u'0.9999963']}}
+```
+
+
+## 六、文本检测、方向分类和文字识别串联Serving推理
+
+### 1. 超轻量中文OCR模型推理
+
+在执行预测时，需要通过参数`image_dir`指定单张图像或者图像集合的路径、参数`det_model_dir`,`cls_model_dir`和`rec_model_dir`分别指定检测，方向分类和识别的inference模型路径。参数`use_angle_cls`用于控制是否启用方向分类模型。与本地预测不同的是，为了减少网络传输耗时，可视化识别结果目前不做处理，用户收到的是推理得到的文字字段。
+
+执行如下命令启动服务端：
+需要注意params.py中的`--use_gpu`的值
+```
+#标准版，Linux用户
+#GPU用户
+python -m paddle_serving_server_gpu.serve --model det_infer_server --port 9293 --gpu_id 0
+python -m paddle_serving_server_gpu.serve --model cls_infer_server --port 9294 --gpu_id 0
+python ocr_rpc_server.py
+#CPU用户
+python -m paddle_serving_server.serve --model det_infer_server --port 9293
+python -m paddle_serving_server.serve --model cls_infer_server --port 9294
+python ocr_rpc_server.py
+
+#快速版，Windows/Linux用户
+python ocr_local_server.py
+```
+
+客户端
+
+```
+python rec_web_client.py
+```
diff --git a/doc/doc_ch/tree.md b/doc/doc_ch/tree.md
new file mode 100644
index 0000000000000000000000000000000000000000..ba8503d119a27b2cac34b593243caa68404b95ec
--- /dev/null
+++ b/doc/doc_ch/tree.md
@@ -0,0 +1,208 @@
+# 整体目录结构
+
+PaddleOCR 的整体目录结构介绍如下：
+
+```
+PaddleOCR
+├── configs // 配置文件，可通过yml文件选择模型结构并修改超参
+│ ├── cls // 方向分类器相关配置文件
+│ │ ├── cls_mv3.yml // 训练配置相关，包括骨干网络、head、loss、优化器
+│ │ └── cls_reader.yml // 数据读取相关，数据读取方式、数据存储路径
+│ ├── det // 检测相关配置文件
+│ │ ├── det_db_icdar15_reader.yml // 数据读取
+│ │ ├── det_mv3_db.yml // 训练配置
+│ │ ...
+│ └── rec // 识别相关配置文件
+│ ├── rec_benchmark_reader.yml // LMDB 格式数据读取相关
+│ ├── rec_chinese_common_train.yml // 通用中文训练配置
+│ ├── rec_icdar15_reader.yml // simple 数据读取相关，包括数据读取函数、数据路径、标签文件
+│ ...
+├── deploy // 部署相关
+│ ├── android_demo // android_demo
+│ │ ...
+│ ├── cpp_infer // C++ infer
+│ │ ├── CMakeLists.txt // Cmake 文件
+│ │ ├── docs // 说明文档
+│ │ │ └── windows_vs2019_build.md
+│ │ ├── include // 头文件
+│ │ │ ├── clipper.h // clipper 库
+│ │ │ ├── config.h // 预测配置
+│ │ │ ├── ocr_cls.h // 方向分类器
+│ │ │ ├── ocr_det.h // 文字检测
+│ │ │ ├── ocr_rec.h // 文字识别
+│ │ │ ├── postprocess_op.h // 检测后处理
+│ │ │ ├── preprocess_op.h // 检测预处理
+│ │ │ └── utility.h // 工具
+│ │ ├── readme.md // 说明文档
+│ │ ├── ...
+│ │ ├── src // 源文件
+│ │ │ ├── clipper.cpp
+│ │ │ ├── config.cpp
+│ │ │ ├── main.cpp
+│ │ │ ├── ocr_cls.cpp
+│ │ │ ├── ocr_det.cpp
+│ │ │ ├── ocr_rec.cpp
+│ │ │ ├── postprocess_op.cpp
+│ │ │ ├── preprocess_op.cpp
+│ │ │ └── utility.cpp
+│ │ └── tools // 编译、执行脚本
+│ │ ├── build.sh // 编译脚本
+│ │ ├── config.txt // 配置文件
+│ │ └── run.sh // 测试启动脚本
+│ ├── docker
+│ │ └── hubserving
+│ │ ├── cpu
+│ │ │ └── Dockerfile
+│ │ ├── gpu
+│ │ │ └── Dockerfile
+│ │ ├── README_cn.md
+│ │ ├── README.md
+│ │ └── sample_request.txt
+│ ├── hubserving // hubserving
+│ │ ├── ocr_det // 文字检测
+│ │ │ ├── config.json // serving 配置
+│ │ │ ├── __init__.py
+│ │ │ ├── module.py // 预测模型
+│ │ │ └── params.py // 预测参数
+│ │ ├── ocr_rec // 文字识别
+│ │ │ ├── config.json
+│ │ │ ├── __init__.py
+│ │ │ ├── module.py
+│ │ │ └── params.py
+│ │ └── ocr_system // 系统预测
+│ │ ├── config.json
+│ │ ├── __init__.py
+│ │ ├── module.py
+│ │ └── params.py
+│ ├── imgs // 预测图片
+│ │ ├── cpp_infer_pred_12.png
+│ │ └── demo.png
+│ ├── ios_demo // ios demo
+│ │ ...
+│ ├── lite // lite 部署
+│ │ ├── cls_process.cc // 方向分类器数据处理
+│ │ ├── cls_process.h
+│ │ ├── config.txt // 检测配置参数
+│ │ ├── crnn_process.cc // crnn数据处理
+│ │ ├── crnn_process.h
+│ │ ├── db_post_process.cc // db数据处理
+│ │ ├── db_post_process.h
+│ │ ├── Makefile // 编译文件
+│ │ ├── ocr_db_crnn.cc // 串联预测
+│ │ ├── prepare.sh // 数据准备
+│ │ ├── readme.md // 说明文档
+│ │ ...
+│ ├── pdserving // pdserving 部署
+│ │ ├── det_local_server.py // 检测 快速版，部署方便预测速度快
+│ │ ├── det_web_server.py // 检测 完整版，稳定性高分布式部署
+│ │ ├── ocr_local_server.py // 检测+识别 快速版
+│ │ ├── ocr_web_client.py // 客户端
+│ │ ├── ocr_web_server.py // 检测+识别 完整版
+│ │ ├── readme.md // 说明文档
+│ │ ├── rec_local_server.py // 识别 快速版
+│ │ └── rec_web_server.py // 识别 完整版
+│ └── slim
+│ └── quantization // 量化相关
+│ ├── export_model.py // 导出模型
+│ ├── quant.py // 量化
+│ └── README.md // 说明文档
+├── doc // 文档教程
+│ ...
+├── paddleocr.py
+├── ppocr // 网络核心代码
+│ ├── data // 数据处理
+│ │ ├── cls // 方向分类器
+│ │ │ ├── dataset_traversal.py // 数据传输，定义数据读取器，读取数据并组成batch
+│ │ │ └── randaugment.py // 随机数据增广操作
+│ │ ├── det // 检测
+│ │ │ ├── data_augment.py // 数据增广操作
+│ │ │ ├── dataset_traversal.py // 数据传输，定义数据读取器，读取数据并组成batch
+│ │ │ ├── db_process.py // db 数据处理
+│ │ │ ├── east_process.py // east 数据处理
+│ │ │ ├── make_border_map.py // 生成边界图
+│ │ │ ├── make_shrink_map.py // 生成收缩图
+│ │ │ ├── random_crop_data.py // 随机切割
+│ │ │ └── sast_process.py // sast 数据处理
+│ │ ├── reader_main.py // 数据读取器主函数
+│ │ └── rec // 识别
+│ │ ├── dataset_traversal.py // 数据传输，定义数据读取器，包含 LMDB_Reader 和 Simple_Reader
+│ │ └── img_tools.py // 数据处理相关，包括数据归一化、扰动
+│ ├── __init__.py
+│ ├── modeling // 组网相关
+│ │ ├── architectures // 模型架构，定义模型所需的各个模块
+│ │ │ ├── cls_model.py // 方向分类器
+│ │ │ ├── det_model.py // 检测
+│ │ │ └── rec_model.py // 识别
+│ │ ├── backbones // 骨干网络
+│ │ │ ├── det_mobilenet_v3.py // 检测 mobilenet_v3
+│ │ │ ├── det_resnet_vd.py
+│ │ │ ├── det_resnet_vd_sast.py
+│ │ │ ├── rec_mobilenet_v3.py // 识别 mobilenet_v3
+│ │ │ ├── rec_resnet_fpn.py
+│ │ │ └── rec_resnet_vd.py
+│ │ ├── common_functions.py // 公共函数
+│ │ ├── heads // 头函数
+│ │ │ ├── cls_head.py // 分类头
+│ │ │ ├── det_db_head.py // db 检测头
+│ │ │ ├── det_east_head.py // east 检测头
+│ │ │ ├── det_sast_head.py // sast 检测头
+│ │ │ ├── rec_attention_head.py // 识别 attention
+│ │ │ ├── rec_ctc_head.py // 识别 ctc
+│ │ │ ├── rec_seq_encoder.py // 识别 序列编码
+│ │ │ ├── rec_srn_all_head.py // 识别 srn 相关
+│ │ │ └── self_attention // srn attention
+│ │ │ └── model.py
+│ │ ├── losses // 损失函数
+│ │ │ ├── cls_loss.py // 方向分类器损失函数
+│ │ │ ├── det_basic_loss.py // 检测基础loss
+│ │ │ ├── det_db_loss.py // DB loss
+│ │ │ ├── det_east_loss.py // EAST loss
+│ │ │ ├── det_sast_loss.py // SAST loss
+│ │ │ ├── rec_attention_loss.py // attention loss
+│ │ │ ├── rec_ctc_loss.py // ctc loss
+│ │ │ └── rec_srn_loss.py // srn loss
+│ │ └── stns // 空间变换网络
+│ │ └── tps.py // TPS 变换
+│ ├── optimizer.py // 优化器
+│ ├── postprocess // 后处理
+│ │ ├── db_postprocess.py // DB 后处理
+│ │ ├── east_postprocess.py // East 后处理
+│ │ ├── lanms // lanms 相关
+│ │ │ ...
+│ │ ├── locality_aware_nms.py // nms
+│ │ └── sast_postprocess.py // sast 后处理
+│ └── utils // 工具
+│ ├── character.py // 字符处理，包括对文本的编码和解码，计算预测准确率
+│ ├── check.py // 参数加载检查
+│ ├── ic15_dict.txt // 英文数字字典，区分大小写
+│ ├── ppocr_keys_v1.txt // 中文字典，用于训练中文模型
+│ ├── save_load.py // 模型保存和加载函数
+│ ├── stats.py // 统计
+│ └── utility.py // 工具函数，包含输入参数是否合法等相关检查工具
+├── README_en.md // 说明文档
+├── README.md
+├── requirments.txt // 安装依赖
+├── setup.py // whl包打包脚本
+└── tools // 启动工具
+ ├── eval.py // 评估函数
+ ├── eval_utils // 评估工具
+ │ ├── eval_cls_utils.py // 分类相关
+ │ ├── eval_det_iou.py // 检测 iou 相关
+ │ ├── eval_det_utils.py // 检测相关
+ │ ├── eval_rec_utils.py // 识别相关
+ │ └── __init__.py
+ ├── export_model.py // 导出 infer 模型
+ ├── infer // 基于预测引擎预测
+ │ ├── predict_cls.py
+ │ ├── predict_det.py
+ │ ├── predict_rec.py
+ │ ├── predict_system.py
+ │ └── utility.py
+ ├── infer_cls.py // 基于训练引擎 预测分类
+ ├── infer_det.py // 基于训练引擎 预测检测
+ ├── infer_rec.py // 基于训练引擎 预测识别
+ ├── program.py // 整体流程
+ ├── test_hubserving.py
+ └── train.py // 启动训练
+
+```
diff --git a/doc/doc_ch/tricks.md b/doc/doc_ch/tricks.md
deleted file mode 100644
index b6852bc95aa3a8eefe9597abc0e173f4515fa358..0000000000000000000000000000000000000000
--- a/doc/doc_ch/tricks.md
+++ /dev/null
@@ -1,68 +0,0 @@
-## 中文OCR训练预测技巧
-这里整理了一些中文OCR训练预测技巧，持续更新中，欢迎各位小伙伴贡献OCR炼丹秘籍～
-- [更换骨干网络](#更换骨干网络)
-- [中文长文本识别](#中文长文本识别)
-- [空格识别](#空格识别)
-
-
-#### 1、更换骨干网络
-- **问题描述**
-
- 目前PaddleOCR中使用的骨干网络有ResNet_vd系列和MobileNetV3系列，更换骨干网络是否有助于效果提升？更换时需要注意什么？
-
-- **炼丹建议**
-
- - 无论是文字检测，还是文字识别，骨干网络的选择是预测效果和预测效率的权衡。一般，选择更大规模的骨干网络，例如ResNet101_vd，则检测或识别更准确，但预测耗时相应也会增加。而选择更小规模的骨干网络，例如MobileNetV3_small_x0_35，则预测更快，但检测或识别的准确率会大打折扣。幸运的是不同骨干网络的检测或识别效果与在ImageNet数据集图像1000分类任务效果正相关。[**飞桨图像分类套件PaddleClas**](https://github.com/PaddlePaddle/PaddleClas)汇总了ResNet_vd、Res2Net、HRNet、MobileNetV3、GhostNet等23种系列的分类网络结构，在上述图像分类任务的top1识别准确率，GPU(V100和T4)和CPU(骁龙855)的预测耗时以及相应的[**117个预训练模型下载地址**](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html)。
- - 文字检测骨干网络的替换，主要是确定类似与ResNet的4个stages，以方便集成后续的类似FPN的检测头。此外，对于文字检测问题，使用ImageNet训练的分类预训练模型，可以加速收敛和效果提升。
- - 文字识别的骨干网络的替换，需要注意网络宽高stride的下降位置。由于文本识别一般宽高比例很大，因此高度下降频率少一些，宽度下降频率多一些。可以参考PaddleOCR中[MobileNetV3骨干网络](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/modeling/backbones/rec_mobilenet_v3.py)的改动。
-
-
-#### 2、中文长文本识别
-- **问题描述**
-
- 中文识别模型训练时分辨率最大是[3,32,320]，如果待识别的文本图像太长，如下图所示，该如何适配？
-
-
-
-
-
-
-
-
-
+
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-## 支持空格的中文OCR效果展示
-
-### 轻量级模型
-
-
-
-
-
+
+
+
+
of the
Whole System\(M\) | Model Size
of the Text
Detector\(M\) | Model Size
of the Direction
Classifier\(M\) | Model Size
of the Text
Recognizer \(M\) | F\-score |
+|:-:|:-:|:-:|:-:|:-:|:-:|
+| ch\_ppocr\_mobile\_v1\.1 | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.5193 |
+| ch\_ppocr\_server\_v1\.1 | 155\.1 | 47\.2 | 0\.9 | 107 | 0\.5414 |
+| ch\_ppocr\_mobile\_v1\.0 | 8\.6 | 4\.1 | \- | 4\.5 | 0\.393 |
+| ch\_ppocr\_server\_v1\.0 | 203\.8 | 98\.5 | \- | 105\.3 | 0\.4436 |
+
+Compares the time-consuming on T4 GPU (ms):
+
+| Model Name | Overall | Text Detector | Direction Classifier | Text Recognizer |
+|:-:|:-:|:-:|:-:|:-:|
+| ch\_ppocr\_mobile\_v1\.1 | 137 | 35 | 24 | 78 |
+| ch\_ppocr\_server\_v1\.1 | 204 | 39 | 25 | 140 |
+| ch\_ppocr\_mobile\_v1\.0 | 117 | 41 | \- | 76 |
+| ch\_ppocr\_server\_v1\.0 | 199 | 52 | \- | 147 |
+
+Compares the time-consuming on CPU (ms):
+
+| Model Name | Overall | Text Detector | Direction Classifier | Text Recognizer |
+|:-:|:-:|:-:|:-:|:-:|
+| ch\_ppocr\_mobile\_v1\.1 | 421 | 164 | 51 | 206 |
+| ch\_ppocr\_mobile\_v1\.0 | 398 | 219 | \- | 179 |
+
+Compares the model size, F-score, the time-consuming on SD 855 of between the slim models and the original models:
+
+| Model Name | Model Size
of the
Whole System\(M\) | Model Size
of the Text
Detector\(M\) | Model Size
of the Direction
Classifier\(M\) | Model Size
of the Text
Recognizer \(M\) | F\-score | SD 855
\(ms\) |
+|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
+| ch\_ppocr\_mobile\_v1\.1 | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.5193 | 306 |
+| ch\_ppocr\_mobile\_slim\_v1\.1 | 3\.5 | 1\.4 | 0\.5 | 1\.6 | 0\.521 | 268 |
diff --git a/doc/doc_en/config_en.md b/doc/doc_en/config_en.md
index b54def895f0758df7cdbd089253d6acd712d2b8e..722da6620fd03912c48a47679e7c13a23f15752e 100644
--- a/doc/doc_en/config_en.md
+++ b/doc/doc_en/config_en.md
@@ -10,7 +10,7 @@ The following list can be viewed via `--help`
## INTRODUCTION TO GLOBAL PARAMETERS OF CONFIGURATION FILE
-Take `rec_chinese_lite_train.yml` as an example
+Take `rec_chinese_lite_train_v1.1.yml` as an example
| Parameter | Use | Default | Note |
@@ -32,6 +32,7 @@ Take `rec_chinese_lite_train.yml` as an example
| loss_type | Set loss type | ctc | Supports two types of loss: ctc / attention |
| distort | Set use distort | false | Support distort type ,read [img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py) |
| use_space_char | Wether to recognize space | false | Only support in character_type=ch mode |
+ label_list | Set the angle supported by the direction classifier | ['0','180'] | Only valid in the direction classifier |
| reader_yml | Set the reader configuration file | ./configs/rec/rec_icdar15_reader.yml | \ |
| pretrain_weights | Load pre-trained model path | ./pretrain_models/CRNN/best_accuracy | \ |
| checkpoints | Load saved model path | None | Used to load saved parameters to continue training after interruption |
diff --git a/doc/doc_en/customize_en.md b/doc/doc_en/customize_en.md
index b63de67c6226abbb5b4fb8d0ed57c19142307203..fb47c14f3346e918f32950c8eec5ada76345ce59 100644
--- a/doc/doc_en/customize_en.md
+++ b/doc/doc_en/customize_en.md
@@ -6,7 +6,7 @@ The process of making a customized ultra-lightweight OCR models can be divided i
PaddleOCR provides two text detection algorithms: EAST and DB. Both support MobileNetV3 and ResNet50_vd backbone networks, select the corresponding configuration file as needed and start training. For example, to train with MobileNetV3 as the backbone network for DB detection model :
```
-python3 tools/train.py -c configs/det/det_mv3_db.yml
+python3 tools/train.py -c configs/det/det_mv3_db.yml 2>&1 | tee det_db.log
```
For more details about data preparation and training tutorials, refer to the documentation [Text detection model training/evaluation/prediction](./detection_en.md)
@@ -14,7 +14,7 @@ For more details about data preparation and training tutorials, refer to the doc
PaddleOCR provides four text recognition algorithms: CRNN, Rosetta, STAR-Net, and RARE. They all support two backbone networks: MobileNetV3 and ResNet34_vd, select the corresponding configuration files as needed to start training. For example, to train a CRNN recognition model that uses MobileNetV3 as the backbone network:
```
-python3 tools/train.py -c configs/rec/rec_chinese_lite_train.yml
+python3 tools/train.py -c configs/rec/rec_chinese_lite_train.yml 2>&1 | tee rec_ch_lite.log
```
For more details about data preparation and training tutorials, refer to the documentation [Text recognition model training/evaluation/prediction](./recognition_en.md)
diff --git a/doc/doc_en/detection_en.md b/doc/doc_en/detection_en.md
index 08e6b63bb77ad1cb5ec4c741d6cad1d099f6c070..156b04b42d6f1f3444f24bb8cd24945cc9ed1fe5 100644
--- a/doc/doc_en/detection_en.md
+++ b/doc/doc_en/detection_en.md
@@ -27,7 +27,7 @@ The provided annotation file format is as follow, seperated by "\t":
" Image file name Image annotation information encoded by json.dumps"
ch4_test_images/img_61.jpg [{"transcription": "MASA", "points": [[310, 104], [416, 141], [418, 216], [312, 179]]}, {...}]
```
-The image annotation after **json.dumps()** encoding is a list containing multiple dictionaries.
+The image annotation after **json.dumps()** encoding is a list containing multiple dictionaries.
The `points` in the dictionary represent the coordinates (x, y) of the four points of the text box, arranged clockwise from the point at the upper left corner.
@@ -38,12 +38,14 @@ If you want to train PaddleOCR on other datasets, please build the annotation fi
## TRAINING
-First download the pretrained model. The detection model of PaddleOCR currently supports two backbones, namely MobileNetV3 and ResNet50_vd. You can use the model in [PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/master/ppcls/modeling/architectures) to replace backbone according to your needs.
+First download the pretrained model. The detection model of PaddleOCR currently supports 3 backbones, namely MobileNetV3, ResNet18_vd and ResNet50_vd. You can use the model in [PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/master/ppcls/modeling/architectures) to replace backbone according to your needs.
```shell
cd PaddleOCR/
# Download the pre-trained model of MobileNetV3
wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x0_5_pretrained.tar
-# Download the pre-trained model of ResNet50
+# or, download the pre-trained model of ResNet18_vd
+wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet18_vd_pretrained.tar
+# or, download the pre-trained model of ResNet50_vd
wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
# decompressing the pre-training model file, take MobileNetV3 as an example
@@ -62,7 +64,7 @@ tar -xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_model
#### START TRAINING
*If CPU version installed, please set the parameter `use_gpu` to `false` in the configuration.*
```shell
-python3 tools/train.py -c configs/det/det_mv3_db.yml
+python3 tools/train.py -c configs/det/det_mv3_db_v1.1.yml 2>&1 | tee train_det.log
```
In the above instruction, use `-c` to select the training to use the `configs/det/det_db_mv3.yml` configuration file.
@@ -70,15 +72,15 @@ For a detailed explanation of the configuration file, please refer to [config](.
You can also use `-o` to change the training parameters without modifying the yml file. For example, adjust the training learning rate to 0.0001
```shell
-python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
+python3 tools/train.py -c configs/det/det_mv3_db_v1.1.yml -o Optimizer.base_lr=0.0001
```
-#### load trained model and conntinue training
+#### load trained model and continue training
If you expect to load trained model and continue the training again, you can specify the parameter `Global.checkpoints` as the model path to be loaded.
For example:
```shell
-python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./your/trained/model
+python3 tools/train.py -c configs/det/det_mv3_db_v1.1.yml -o Global.checkpoints=./your/trained/model
```
**Note**: The priority of `Global.checkpoints` is higher than that of `Global.pretrain_weights`, that is, when two parameters are specified at the same time, the model specified by `Global.checkpoints` will be loaded first. If the model path specified by `Global.checkpoints` is wrong, the one specified by `Global.pretrain_weights` will be loaded.
@@ -88,18 +90,18 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./you
PaddleOCR calculates three indicators for evaluating performance of OCR detection task: Precision, Recall, and Hmean.
-Run the following code to calculate the evaluation indicators. The result will be saved in the test result file specified by `save_res_path` in the configuration file `det_db_mv3.yml`
+Run the following code to calculate the evaluation indicators. The result will be saved in the test result file specified by `save_res_path` in the configuration file `det_db_mv3_v1.1.yml`
When evaluating, set post-processing parameters `box_thresh=0.6`, `unclip_ratio=1.5`. If you use different datasets, different models for training, these two parameters should be adjusted for better result.
```shell
-python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="{path/to/weights}/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
+python3 tools/eval.py -c configs/det/det_mv3_db_v1.1.yml -o Global.checkpoints="{path/to/weights}/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
```
The model parameters during training are saved in the `Global.save_model_dir` directory by default. When evaluating indicators, you need to set `Global.checkpoints` to point to the saved parameter file.
Such as:
```shell
-python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
+python3 tools/eval.py -c configs/det/det_mv3_db_v1.1.yml -o Global.checkpoints="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
```
* Note: `box_thresh` and `unclip_ratio` are parameters required for DB post-processing, and not need to be set when evaluating the EAST model.
@@ -108,16 +110,16 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="./ou
Test the detection result on a single image:
```shell
-python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o TestReader.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy"
+python3 tools/infer_det.py -c configs/det/det_mv3_db_v1.1.yml -o TestReader.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy"
```
When testing the DB model, adjust the post-processing threshold:
```shell
-python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o TestReader.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
+python3 tools/infer_det.py -c configs/det/det_mv3_db_v1.1.yml -o TestReader.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
```
Test the detection result on all images in the folder:
```shell
-python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o TestReader.infer_img="./doc/imgs_en/" Global.checkpoints="./output/det_db/best_accuracy"
+python3 tools/infer_det.py -c configs/det/det_mv3_db_v1.1.yml -o TestReader.infer_img="./doc/imgs_en/" Global.checkpoints="./output/det_db/best_accuracy"
```
diff --git a/doc/doc_en/inference_en.md b/doc/doc_en/inference_en.md
index 83ec2a90c45a320815e10e8572d894068c0b5130..609b65fa55a743acd72407a34288afc793885d3c 100644
--- a/doc/doc_en/inference_en.md
+++ b/doc/doc_en/inference_en.md
@@ -5,32 +5,38 @@ The inference model (the model saved by `fluid.io.save_inference_model`) is gene
The model saved during the training process is the checkpoints model, which saves the parameters of the model and is mostly used to resume training.
-Compared with the checkpoints model, the inference model will additionally save the structural information of the model. It has superior performance in predicting in deployment and accelerating inferencing, is flexible and convenient, and is suitable for integration with actual systems. For more details, please refer to the document [Classification Framework](https://paddleclas.readthedocs.io/zh_CN/latest/extension/paddle_inference.html).
+Compared with the checkpoints model, the inference model will additionally save the structural information of the model. It has superior performance in predicting in deployment and accelerating inferencing, is flexible and convenient, and is suitable for integration with actual systems. For more details, please refer to the document [Classification Framework](https://github.com/PaddlePaddle/PaddleClas/blob/master/docs/zh_CN/extension/paddle_inference.md).
Next, we first introduce how to convert a trained model into an inference model, and then we will introduce text detection, text recognition, and the concatenation of them based on inference model.
- [CONVERT TRAINING MODEL TO INFERENCE MODEL](#CONVERT)
- [Convert detection model to inference model](#Convert_detection_model)
- [Convert recognition model to inference model](#Convert_recognition_model)
-
-
+ - [Convert angle classification model to inference model](#Convert_angle_class_model)
+
+
- [TEXT DETECTION MODEL INFERENCE](#DETECTION_MODEL_INFERENCE)
- [1. LIGHTWEIGHT CHINESE DETECTION MODEL INFERENCE](#LIGHTWEIGHT_DETECTION)
- [2. DB TEXT DETECTION MODEL INFERENCE](#DB_DETECTION)
- [3. EAST TEXT DETECTION MODEL INFERENCE](#EAST_DETECTION)
- [4. SAST TEXT DETECTION MODEL INFERENCE](#SAST_DETECTION)
-
+ - [5. Multilingual model inference](#Multilingual model inference)
+
- [TEXT RECOGNITION MODEL INFERENCE](#RECOGNITION_MODEL_INFERENCE)
- [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_RECOGNITION)
- [2. CTC-BASED TEXT RECOGNITION MODEL INFERENCE](#CTC-BASED_RECOGNITION)
- [3. ATTENTION-BASED TEXT RECOGNITION MODEL INFERENCE](#ATTENTION-BASED_RECOGNITION)
- - [4. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY](#USING_CUSTOM_CHARACTERS)
-
-
-- [TEXT DETECTION AND RECOGNITION INFERENCE CONCATENATION](#CONCATENATION)
+ - [4. SRN-BASED TEXT RECOGNITION MODEL INFERENCE](#SRN-BASED_RECOGNITION)
+ - [5. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY](#USING_CUSTOM_CHARACTERS)
+ - [6. MULTILINGUAL MODEL INFERENCE](MULTILINGUAL_MODEL_INFERENCE)
+
+- [ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE)
+ - [1. ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE)
+
+- [TEXT DETECTION ANGLE CLASSIFICATION AND RECOGNITION INFERENCE CONCATENATION](#CONCATENATION)
- [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_CHINESE_MODEL)
- [2. OTHER MODELS](#OTHER_MODELS)
-
+
## CONVERT TRAINING MODEL TO INFERENCE MODEL
@@ -38,8 +44,9 @@ Next, we first introduce how to convert a trained model into an inference model,
Download the lightweight Chinese detection model:
```
-wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar && tar xf ./ch_lite/ch_det_mv3_db.tar -C ./ch_lite/
+wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v1.1_det_train.tar -C ./ch_lite/
```
+
The above model is a DB algorithm trained with MobileNetV3 as the backbone. To convert the trained model into an inference model, just run the following command:
```
# -c Set the training algorithm yml configuration file
@@ -47,9 +54,9 @@ The above model is a DB algorithm trained with MobileNetV3 as the backbone. To c
# Global.checkpoints parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
# Global.save_inference_dir Set the address where the converted model will be saved.
-python3 tools/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./ch_lite/det_mv3_db/best_accuracy \
- Global.save_inference_dir=./inference/det_db/
+python3 tools/export_model.py -c configs/det/det_mv3_db_v1.1.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v1.1_det_train/best_accuracy Global.save_inference_dir=./inference/det_db/
```
+
When converting to an inference model, the configuration file used is the same as the configuration file used during training. In addition, you also need to set the `Global.checkpoints` and `Global.save_inference_dir` parameters in the configuration file.
`Global.checkpoints` points to the model parameter file saved during training, and `Global.save_inference_dir` is the directory where the generated inference model is saved.
After the conversion is successful, there are two files in the `save_inference_dir` directory:
@@ -64,7 +71,7 @@ inference/det_db/
Download the lightweight Chinese recognition model:
```
-wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar && tar xf ./ch_lite/ch_rec_mv3_crnn.tar -C ./ch_lite/
+wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_train.tar && tar xf ch_ppocr_mobile_v1.1_rec_train.tar -C ./ch_lite/
```
The recognition model is converted to the inference model in the same way as the detection, as follows:
@@ -74,8 +81,7 @@ The recognition model is converted to the inference model in the same way as the
# Global.checkpoints parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
# Global.save_inference_dir Set the address where the converted model will be saved.
-python3 tools/export_model.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints=./ch_lite/rec_mv3_crnn/best_accuracy \
- Global.save_inference_dir=./inference/rec_crnn/
+python3 tools/export_model.py -c configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v1.1_rec_train/best_accuracy \
```
If you have a model trained on your own dataset with a different dictionary file, please make sure that you modify the `character_dict_path` in the configuration file to your dictionary file path.
@@ -87,6 +93,33 @@ After the conversion is successful, there are two files in the directory:
└─ params Identify the parameter files of the inference model
```
+
+### Convert angle classification model to inference model
+
+Download the angle classification model:
+```
+wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v1.1_cls_train.tar -C ./ch_lite/
+```
+
+The angle classification model is converted to the inference model in the same way as the detection, as follows:
+```
+# -c Set the training algorithm yml configuration file
+# -o Set optional parameters
+# Global.checkpoints parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
+# Global.save_inference_dir Set the address where the converted model will be saved.
+
+python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v1.1_cls_train/best_accuracy \
+ Global.save_inference_dir=./inference/cls/
+```
+
+After the conversion is successful, there are two files in the directory:
+```
+/inference/cls/
+ └─ model Identify the saved model files
+ └─ params Identify the parameter files of the inference model
+```
+
+
## TEXT DETECTION MODEL INFERENCE
@@ -268,24 +301,83 @@ self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
dict_character = list(self.character_str)
```
+
+### 4. SRN-BASED TEXT RECOGNITION MODEL INFERENCE
+
+The recognition model based on SRN requires additional setting of the recognition algorithm parameter --rec_algorithm="SRN".
+At the same time, it is necessary to ensure that the predicted shape is consistent with the training, such as: --rec_image_shape="1, 64, 256"
+
+```
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" \
+ --rec_model_dir="./inference/srn/" \
+ --rec_image_shape="1, 64, 256" \
+ --rec_char_type="en" \
+ --rec_algorithm="SRN"
+```
+
+
-### 4. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY
+### 5. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY
If the chars dictionary is modified during training, you need to specify the new dictionary path by setting the parameter `rec_char_dict_path` when using your inference model to predict.
```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_type="en" --rec_char_dict_path="your text dict path"
```
+
+### 6. MULTILINGAUL MODEL INFERENCE
+If you need to predict other language models, when using inference model prediction, you need to specify the dictionary path used by `--rec_char_dict_path`. At the same time, in order to get the correct visualization results,
+You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/` path, such as Korean recognition:
+
+```
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/korean.ttf"
+```
+
+
+After executing the command, the prediction result of the above figure is:
+
+``` text
+2020-09-19 16:15:05,076-INFO: index: [205 206 38 39]
+2020-09-19 16:15:05,077-INFO: word : 바탕으로
+2020-09-19 16:15:05,077-INFO: score: 0.9171358942985535
+```
+
+
+## ANGLE CLASSIFICATION MODEL INFERENCE
+
+The following will introduce the angle classification model inference.
+
+
+
+### 1.ANGLE CLASSIFICATION MODEL INFERENCE
+
+For angle classification model inference, you can execute the following commands:
+
+```
+python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --cls_model_dir="./inference/cls/"
+```
+
+
+
+After executing the command, the prediction results (classification angle and score) of the above image will be printed on the screen.
+
+Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999963]
+
+
-## TEXT DETECTION AND RECOGNITION INFERENCE CONCATENATION
+## TEXT DETECTION ANGLE CLASSIFICATION AND RECOGNITION INFERENCE CONCATENATION
### 1. LIGHTWEIGHT CHINESE MODEL
-When performing prediction, you need to specify the path of a single image or a folder of images through the parameter `image_dir`, the parameter `det_model_dir` specifies the path to detect the inference model, and the parameter `rec_model_dir` specifies the path to identify the inference model. The visualized recognition results are saved to the `./inference_results` folder by default.
+When performing prediction, you need to specify the path of a single image or a folder of images through the parameter `image_dir`, the parameter `det_model_dir` specifies the path to detect the inference model, the parameter `cls_model_dir` specifies the path to angle classification inference model and the parameter `rec_model_dir` specifies the path to identify the inference model. The parameter `use_angle_cls` is used to control whether to enable the angle classification model.The visualized recognition results are saved to the `./inference_results` folder by default.
```
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/"
+# use direction classifier
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=true
+
+# not use use direction classifier
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/"
```
After executing the command, the recognition result image is as follows:
diff --git a/doc/doc_en/installation_en.md b/doc/doc_en/installation_en.md
index 37f66b05e72eee46cdb25dba758b84ff5037c4eb..17d629c1a316652293149c73225ad086f83079cc 100644
--- a/doc/doc_en/installation_en.md
+++ b/doc/doc_en/installation_en.md
@@ -3,11 +3,11 @@
After testing, paddleocr can run on glibc 2.23. You can also test other glibc versions or install glic 2.23 for the best compatibility.
PaddleOCR working environment:
-- PaddlePaddle1.7
+- PaddlePaddle1.8+, Recommend PaddlePaddle 2.0.0.beta
- python3.7
- glibc 2.23
-It is recommended to use the docker provided by us to run PaddleOCR, please refer to the use of docker [link](https://docs.docker.com/get-started/).
+It is recommended to use the docker provided by us to run PaddleOCR, please refer to the use of docker [link](https://www.runoob.com/docker/docker-tutorial.html/).
*If you want to directly run the prediction code on mac or windows, you can start from step 2.*
@@ -49,18 +49,15 @@ docker images
hub.baidubce.com/paddlepaddle/paddle latest-gpu-cuda9.0-cudnn7-dev f56310dcc829
```
-**2. Install PaddlePaddle Fluid v1.7 (the higher version is not supported yet, the adaptation work is in progress)**
+**2. Install PaddlePaddle Fluid v2.0**
```
pip3 install --upgrade pip
-# If you have cuda9 installed on your machine, please run the following command to install
-python3 -m pip install paddlepaddle-gpu==1.7.2.post97 -i https://pypi.tuna.tsinghua.edu.cn/simple
-
-# If you have cuda10 installed on your machine, please run the following command to install
-python3 -m pip install paddlepaddle-gpu==1.7.2.post107 -i https://pypi.tuna.tsinghua.edu.cn/simple
+# If you have cuda9 or cuda10 installed on your machine, please run the following command to install
+python3 -m pip install paddlepaddle-gpu==2.0.0b0 -i https://mirror.baidu.com/pypi/simple
# If you only have cpu on your machine, please run the following command to install
-python3 -m pip install paddlepaddle==1.7.2 -i https://pypi.tuna.tsinghua.edu.cn/simple
+python3 -m pip install paddlepaddle==2.0.0b0 -i https://mirror.baidu.com/pypi/simple
```
For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/install/quick) for operation.
diff --git a/doc/doc_en/models_list_en.md b/doc/doc_en/models_list_en.md
new file mode 100644
index 0000000000000000000000000000000000000000..3d3cdc1d6c2e2a52c64f6ed0fd502456271173b6
--- /dev/null
+++ b/doc/doc_en/models_list_en.md
@@ -0,0 +1,70 @@
+## OCR model list（V1.1, updated on 9.22）
+
+- [1. Text Detection Model](#Detection)
+- [2. Text Recognition Model](#Recognition)
+ - [Chinese Recognition Model](#Chinese)
+ - [English Recognition Model](#English)
+ - [Multilingual Recognition Model](#Multilingual)
+- [3. Text Angle Classification Model](#Angle)
+
+The downloadable models provided by PaddleOCR include `inference model`, `trained model`, `pre-trained model` and `slim model`. The differences between the models are as follows:
+
+|model type|model format|description|
+|-|-|-|
+|inference model|model、params|Used for reasoning based on Python prediction engine. [detail](./inference_en.md)|
+|trained model / pre-trained model|\*.pdmodel、\*.pdopt、\*.pdparams|The checkpoints model saved in the training process, which stores the parameters of the model, mostly used for model evaluation and continuous training.|
+|slim model|\*.nb|Generally used for Lite deployment|
+
+
+
+### 1. Text Detection Model
+|model name|description|config|model size|download|
+|-|-|-|-|-|
+|ch_ppocr_mobile_slim_v1.1_det|Slim pruned lightweight model, supporting Chinese, English, multilingual text detection|[det_mv3_db_v1.1.yml](../../configs/det/det_mv3_db_v1.1.yml)|1.4M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_infer.tar) / [slim model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_det_prune_opt.nb)|
+|ch_ppocr_mobile_v1.1_det|Original lightweight model, supporting Chinese, English, multilingual text detection|[det_mv3_db_v1.1.yml](../../configs/det/det_mv3_db_v1.1.yml)|2.6M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar)|
+|ch_ppocr_server_v1.1_det|General model, which is larger than the lightweight model, but achieved better performance|[det_r18_vd_db_v1.1.yml](../../configs/det/det_r18_vd_db_v1.1.yml)|47.2M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_train.tar)|
+
+
+
+### 2. Text Recognition Model
+
+
+#### Chinese Recognition Model
+|model name|description|config|model size|download|
+|-|-|-|-|-|
+|ch_ppocr_mobile_slim_v1.1_rec|Slim pruned and quantized lightweight model, supporting Chinese, English and number recognition|[rec_chinese_lite_train_v1.1.yml](../../configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml)|1.6M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_infer.tar) / [slim model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_rec_quant_opt.nb) |
+|ch_ppocr_mobile_v1.1_rec|Original lightweight model, supporting Chinese, English and number recognition|[rec_chinese_lite_train_v1.1.yml](../../configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml)|4.6M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_train.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_pre.tar) |
+|ch_ppocr_server_v1.1_rec|General model, supporting Chinese, English and number recognition|[rec_chinese_common_train_v1.1.yml](../../configs/rec/ch_ppocr_v1.1/rec_chinese_common_train_v1.1.yml)|105M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_train.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_pre.tar) |
+
+**Note:** The `trained model` is finetuned on the `pre-trained model` with real data and synthsized vertical text data, which achieved better performance in real scene. The `pre-trained model` is directly trained on the full amount of real data and synthsized data, which is more suitable for finetune on your own dataset.
+
+
+#### English Recognition Model
+|model name|description|config|model size|download|
+|-|-|-|-|-|
+|en_ppocr_mobile_slim_v1.1_rec|Slim pruned and quantized lightweight model, supporting English and number recognition|[rec_en_lite_train.yml](../../configs/rec/multi_languages/rec_en_lite_train.yml)|0.9M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/en/en_ppocr_mobile_v1.1_rec_quant_infer.tar) / [slim model](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/en/en_ppocr_mobile_v1.1_rec_quant_opt.nb) |
+|en_ppocr_mobile_v1.1_rec|Original lightweight model, supporting English and number recognition|[rec_en_lite_train.yml](../../configs/rec/multi_languages/rec_en_lite_train.yml)|2.0M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/en/en_ppocr_mobile_v1.1_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/en/en_ppocr_mobile_v1.1_rec_train.tar) |
+
+
+#### Multilingual Recognition Model（Updating...）
+|model name|description|config|model size|download|
+|-|-|-|-|-|
+| french_ppocr_mobile_v1.1_rec |Lightweight model for French recognition|[rec_french_lite_train.yml](../../configs/rec/multi_languages/rec_french_lite_train.yml)|2.1M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/fr/french_ppocr_mobile_v1.1_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/fr/french_ppocr_mobile_v1.1_rec_train.tar) |
+| german_ppocr_mobile_v1.1_rec |German model for French recognition|[rec_ger_lite_train.yml](../../configs/rec/multi_languages/rec_ger_lite_train.yml)|2.1M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/ge/german_ppocr_mobile_v1.1_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/ge/german_ppocr_mobile_v1.1_rec_train.tar) |
+| korean_ppocr_mobile_v1.1_rec |Lightweight model for Korean recognition|[rec_korean_lite_train.yml](../../configs/rec/multi_languages/rec_korean_lite_train.yml)|3.4M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/kr/korean_ppocr_mobile_v1.1_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/kr/korean_ppocr_mobile_v1.1_rec_train.tar) |
+| japan_ppocr_mobile_v1.1_rec |Lightweight model for Japanese recognition|[rec_japan_lite_train.yml](../../configs/rec/multi_languages/rec_japan_lite_train.yml)|3.7M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/jp/japan_ppocr_mobile_v1.1_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/jp/japan_ppocr_mobile_v1.1_rec_train.tar) |
+
+
+
+### 3. Text Angle Classification Model
+|model name|description|config|model size|download|
+|-|-|-|-|-|
+|ch_ppocr_mobile_v1.1_cls_quant|Slim quantized model|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)|0.5M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_quant_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_quant_train.tar) / [slim model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_cls_quant_opt.nb) |
+|ch_ppocr_mobile_v1.1_cls|Original model|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)|850kb|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar) |
+
+
+## OCR model list（V1.0, updated on 7.16）
+|model name|description|detection model|recognition model|recognition model supporting space recognition|
+|-|-|-|-|-|
+|chinese_db_crnn_mobile|8.6M lightweight OCR model|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar) | [inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar) |[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar)
+|chinese_db_crnn_server|General OCR model|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar) | [inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar) |[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)
diff --git a/doc/doc_en/quickstart_en.md b/doc/doc_en/quickstart_en.md
index d1fa1683fcfea14be477c910fb2a8dc7709c5d36..6a5f369349f3142c29dbe7edb766a51a0789cf37 100644
--- a/doc/doc_en/quickstart_en.md
+++ b/doc/doc_en/quickstart_en.md
@@ -5,16 +5,19 @@
Please refer to [quick installation](./installation_en.md) to configure the PaddleOCR operating environment.
-*Note: Support the use of PaddleOCR through whl package installation，pelease refer [PaddleOCR Package](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/whl_en.md)。*
+* Note: Support the use of PaddleOCR through whl package installation，pelease refer [PaddleOCR Package](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/whl_en.md).
## 2.inference models
-| Name | Introduction | Detection model | Recognition model | Recognition model with space support |
-|-|-|-|-|-|
-|chinese_db_crnn_mobile| Ultra-lightweight Chinese OCR model |[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar)
-|chinese_db_crnn_server| Universal Chinese OCR model |[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)
+The detection and recognition models on the mobile and server sides are as follows. For more models (including multiple languages), please refer to [PP-OCR v1.1 series model list](../doc_ch/models_list.md)
-* If wget is not installed in the windows environment, you can copy the link to the browser to download when downloading the model, and uncompress it and place it in the corresponding directory.
+
+| Model introduction | Model name | Recommended scene | Detection model | Direction Classifier | Recognition model |
+| ------------ | --------------- | ----------------|---- | ---------- | -------- |
+| Ultra-lightweight Chinese OCR model（8.1M） | ch_ppocr_mobile_v1.1_xx |Mobile-side/Server-side|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar)|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_pre.tar) |
+| Universal Chinese OCR model（155.1M） |ch_ppocr_server_v1.1_xx|Server-side |[inference model](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_pre.tar) |
+
+* If `wget` is not installed in the windows environment, you can copy the link to the browser to download when downloading the model, then uncompress it and place it in the corresponding directory.
Copy the download address of the `inference model` for detection and recognition in the table above, and uncompress them.
@@ -24,6 +27,8 @@ mkdir inference && cd inference
wget {url/of/detection/inference_model} && tar xf {name/of/detection/inference_model/package}
# Download the recognition model and unzip
wget {url/of/recognition/inference_model} && tar xf {name/of/recognition/inference_model/package}
+# Download the direction classifier model and unzip
+wget {url/of/classification/inference_model} && tar xf {name/of/classification/inference_model/package}
cd ..
```
@@ -32,9 +37,11 @@ Take the ultra-lightweight model as an example:
```
mkdir inference && cd inference
# Download the detection model of the ultra-lightweight Chinese OCR model and uncompress it
-wget https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar && tar xf ch_det_mv3_db_infer.tar
+wget https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar && tar xf ch_ppocr_mobile_v1.1_det_infer.tar
# Download the recognition model of the ultra-lightweight Chinese OCR model and uncompress it
-wget https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar && tar xf ch_rec_mv3_crnn_infer.tar
+wget https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar && tar xf ch_ppocr_mobile_v1.1_rec_infer.tar
+# Download the direction classifier model of the ultra-lightweight Chinese OCR model and uncompress it
+wget https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar && tar xf ch_ppocr_mobile_v1.1_cls_infer.tar
cd ..
```
@@ -42,10 +49,13 @@ After decompression, the file structure should be as follows:
```
|-inference
- |-ch_rec_mv3_crnn
+ |-ch_ppocr_mobile_v1.1_det_infer
+ |- model
+ |- params
+ |-ch_ppocr_mobile_v1.1_rec_infer
|- model
|- params
- |-ch_det_mv3_db
+ |-ch_ppocr_mobile_v1.1_cls_infer
|- model
|- params
...
@@ -53,20 +63,20 @@ After decompression, the file structure should be as follows:
## 3. Single image or image set prediction
-* The following code implements text detection and recognition process. When performing prediction, you need to specify the path of a single image or image set through the parameter `image_dir`, the parameter `det_model_dir` specifies the path to detect the inference model, and the parameter `rec_model_dir` specifies the path to identify the inference model. The visual results are saved to the `./inference_results` folder by default.
+* The following code implements text detection and recognition process. When performing prediction, you need to specify the path of a single image or image set through the parameter `image_dir`, the parameter `det_model_dir` specifies the path to detect the inference model, the parameter `rec_model_dir` specifies the path to identify the inference model, the parameter `use_angle_cls` specifies whether to use the direction classifier, the parameter `cls_model_dir` specifies the path to identify the direction classifier model, the parameter `use_space_char` specifies whether to predict the space char. The visual results are saved to the `./inference_results` folder by default.
```bash
# Predict a single image specified by image_dir
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_det_mv3_db/" --rec_model_dir="./inference/ch_rec_mv3_crnn/"
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_mobile_v1.1_det_infer/" --rec_model_dir="./inference/ch_ppocr_mobile_v1.1_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v1.1_cls_infer/" --use_angle_cls=True --use_space_char=True
# Predict imageset specified by image_dir
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/" --det_model_dir="./inference/ch_det_mv3_db/" --rec_model_dir="./inference/ch_rec_mv3_crnn/"
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/" --det_model_dir="./inference/ch_ppocr_mobile_v1.1_det_infer/" --rec_model_dir="./inference/ch_ppocr_mobile_v1.1_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v1.1_cls_infer/" --use_angle_cls=True --use_space_char=True
# If you want to use the CPU for prediction, you need to set the use_gpu parameter to False
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_det_mv3_db/" --rec_model_dir="./inference/ch_rec_mv3_crnn/" --use_gpu=False
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_mobile_v1.1_det_infer/" --rec_model_dir="./inference/ch_ppocr_mobile_v1.1_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v1.1_cls_infer/" --use_angle_cls=True --use_space_char=True --use_gpu=False
```
- Universal Chinese OCR model
@@ -75,25 +85,18 @@ Please follow the above steps to download the corresponding models and update th
```
# Predict a single image specified by image_dir
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_det_r50_vd_db/" --rec_model_dir="./inference/ch_rec_r34_vd_crnn/"
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_server_v1.1_det_infer/" --rec_model_dir="./inference/ch_ppocr_server_v1.1_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v1.1_cls_infer/" --use_angle_cls=True --use_space_char=True
```
-- Universal Chinese OCR model with the support of space
-
-Please follow the above steps to download the corresponding models and update the relevant parameters, The example is as follows.
+* Note
+ - If you want to use the recognition model which does not support space char recognition, please update the source code to the latest version and add parameters `--use_space_char=False`.
+ - If you do not want to use direction classifier, please update the source code to the latest version and add parameters `--use_angle_cls=False`.
-* Note: Please update the source code to the latest version and add parameters `--use_space_char=True`
-
-```
-# Predict a single image specified by image_dir
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_12.jpg" --det_model_dir="./inference/ch_det_r50_vd_db/" --rec_model_dir="./inference/ch_rec_r34_vd_crnn_enhance/" --use_space_char=True
-```
-
For more text detection and recognition tandem reasoning, please refer to the document tutorial
: [Inference with Python inference engine](./inference_en.md)。
In addition, the tutorial also provides other deployment methods for the Chinese OCR model:
- [Server-side C++ inference](../../deploy/cpp_infer/readme_en.md)
-- [Service deployment](./serving_en.md)
+- [Service deployment](../../deploy/pdserving/readme_en.md)
- [End-to-end deployment](../../deploy/lite/readme_en.md)
diff --git a/doc/doc_en/recognition_en.md b/doc/doc_en/recognition_en.md
index b9c42afada05950539d4dbfb45e133af946b1aae..41b00c52a7780d02c144c251553f427e5b875e5e 100644
--- a/doc/doc_en/recognition_en.md
+++ b/doc/doc_en/recognition_en.md
@@ -1,5 +1,22 @@
## TEXT RECOGNITION
+- [DATA PREPARATION](#DATA_PREPARATION)
+ - [Dataset Download](#Dataset_download)
+ - [Costom Dataset](#Costom_Dataset)
+ - [Dictionary](#Dictionary)
+ - [Add Space Category](#Add_space_category)
+
+- [TRAINING](#TRAINING)
+ - [Data Augmentation](#Data_Augmentation)
+ - [Training](#Training)
+ - [Multi-language](#Multi_language)
+
+- [EVALUATION](#EVALUATION)
+
+- [PREDICTION](#PREDICTION)
+ - [Training engine prediction](#Training_engine_prediction)
+
+
### DATA PREPARATION
@@ -13,13 +30,14 @@ The default storage path for training data is `PaddleOCR/train_data`, if you alr
ln -sf /train_data/dataset
```
-
+
* Dataset download
If you do not have a dataset locally, you can download it on the official website [icdar2015](http://rrc.cvc.uab.es/?ch=4&com=downloads). Also refer to [DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here)，download the lmdb format dataset required for benchmark
If you want to reproduce the paper indicators of SRN, you need to download offline [augmented data](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA), extraction code: y3ry. The augmented data is obtained by rotation and perturbation of mjsynth and synthtext. Please unzip the data to {your_path}/PaddleOCR/train_data/data_lmdb_Release/training/path.
+
* Use your own dataset:
If you want to use your own data for training, please refer to the following to organize your data.
@@ -72,7 +90,7 @@ Similar to the training set, the test set also needs to be provided a folder con
|- word_003.jpg
| ...
```
-
+
- Dictionary
Finally, a dictionary ({word_dict_name}.txt) needs to be provided so that when the model is trained, all the characters that appear can be mapped to the dictionary index.
@@ -92,9 +110,21 @@ In `word_dict.txt`, there is a single word in each line, which maps characters a
`ppocr/utils/ppocr_keys_v1.txt` is a Chinese dictionary with 6623 characters.
-`ppocr/utils/ic15_dict.txt` is an English dictionary with 36 characters.
+`ppocr/utils/ic15_dict.txt` is an English dictionary with 63 characters
+
+`ppocr/utils/dict/french_dict.txt` is a French dictionary with 118 characters
+
+`ppocr/utils/dict/japan_dict.txt` is a French dictionary with 4399 characters
+
+`ppocr/utils/dict/korean_dict.txt` is a French dictionary with 3636 characters
+
+`ppocr/utils/dict/german_dict.txt` is a French dictionary with 131 characters
+
+You can use it on demand.
+
+The current multi-language model is still in the demo stage and will continue to optimize the model and add languages. **You are very welcome to provide us with dictionaries and fonts in other languages**,
+If you like, you can submit the dictionary file to [dict](../../ppocr/utils/dict) or corpus file to [corpus](../../ppocr/utils/corpus) and we will thank you in the Repo.
-You can use them if needed.
To customize the dict file, please modify the `character_dict_path` field in `configs/rec/rec_icdar15_train.yml` and set `character_type` to `ch`.
@@ -102,12 +132,14 @@ To customize the dict file, please modify the `character_dict_path` field in `co
If you need to customize dic file, please add character_dict_path field in configs/rec/rec_icdar15_train.yml to point to your dictionary path. And set character_type to ch.
+
- Add space category
If you want to support the recognition of the `space` category, please set the `use_space_char` field in the yml file to `true`.
**Note: use_space_char only takes effect when character_type=ch**
+
### TRAINING
PaddleOCR provides training scripts, evaluation scripts, and prediction scripts. In this section, the CRNN recognition model will be used as an example:
@@ -126,14 +158,12 @@ tar -xf rec_mv3_none_bilstm_ctc.tar && rm -rf rec_mv3_none_bilstm_ctc.tar
Start training:
```
-# Set PYTHONPATH path
-export PYTHONPATH=$PYTHONPATH:.
# GPU training Support single card and multi-card training, specify the card number through CUDA_VISIBLE_DEVICES
export CUDA_VISIBLE_DEVICES=0,1,2,3
-# Training icdar15 English data
-python3 tools/train.py -c configs/rec/rec_icdar15_train.yml
+# Training icdar15 English data and saving the log as train_rec.log
+python3 tools/train.py -c configs/rec/rec_icdar15_train.yml 2>&1 | tee train_rec.log
```
-
+
- Data Augmentation
PaddleOCR provides a variety of data augmentation methods. If you want to add disturbance during training, please set `distort: true` in the configuration file.
@@ -142,7 +172,7 @@ The default perturbation methods are: cvtColor, blur, jitter, Gasuss noise, rand
Each disturbance method is selected with a 50% probability during the training process. For specific code implementation, please refer to: [img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py)
-
+
- Training
PaddleOCR supports alternating training and evaluation. You can modify `eval_batch_step` in `configs/rec/rec_icdar15_train.yml` to set the evaluation frequency. By default, it is evaluated every 500 iter and the best acc model is saved under `output/rec_CRNN/best_accuracy` during the evaluation process.
@@ -154,7 +184,10 @@ If the evaluation set is large, the test will be time-consuming. It is recommend
| Configuration file | Algorithm | backbone | trans | seq | pred |
| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: |
+| [rec_chinese_lite_train_v1.1.yml](../../configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml) | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc |
+| [rec_chinese_common_train_v1.1.yml](../../configs/rec/ch_ppocr_v1.1/rec_chinese_common_train_v1.1.yml) | CRNN | ResNet34_vd | None | BiLSTM | ctc |
| rec_chinese_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc |
+| rec_chinese_common_train.yml | CRNN | ResNet34_vd | None | BiLSTM | ctc |
| rec_icdar15_train.yml | CRNN | Mobilenet_v3 large 0.5 | None | BiLSTM | ctc |
| rec_mv3_none_bilstm_ctc.yml | CRNN | Mobilenet_v3 large 0.5 | None | BiLSTM | ctc |
| rec_mv3_none_none_ctc.yml | Rosetta | Mobilenet_v3 large 0.5 | None | None | ctc |
@@ -165,7 +198,8 @@ If the evaluation set is large, the test will be time-consuming. It is recommend
| rec_r34_vd_tps_bilstm_attn.yml | RARE | Resnet34_vd | tps | BiLSTM | attention |
| rec_r34_vd_tps_bilstm_ctc.yml | STARNet | Resnet34_vd | tps | BiLSTM | ctc |
-For training Chinese data, it is recommended to use `rec_chinese_lite_train.yml`. If you want to try the result of other algorithms on the Chinese data set, please refer to the following instructions to modify the configuration file:
+For training Chinese data, it is recommended to use
+训练中文数据，推荐使用[rec_chinese_lite_train_v1.1.yml](../../configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml). If you want to try the result of other algorithms on the Chinese data set, please refer to the following instructions to modify the configuration file:
co
Take `rec_mv3_none_none_ctc.yml` as an example:
```
@@ -201,8 +235,43 @@ Optimizer:
```
**Note that the configuration file for prediction/evaluation must be consistent with the training.**
+
+- Multi-language
+
+PaddleOCR also provides multi-language. The configuration file in `configs/rec/multi_languages` provides multi-language configuration files. Currently, the multi-language algorithms supported by PaddleOCR are:
+| Configuration file | Algorithm name | backbone | trans | seq | pred | language ｜
+| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: | :-----: |
+| rec_en_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | English ｜
+| rec_french_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | French ｜
+| rec_ger_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | German ｜
+| rec_japan_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Japanese ｜
+| rec_korean_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Korean ｜
+
+The multi-language model training method is the same as the Chinese model. The training data set is 100w synthetic data. A small amount of fonts and test data can be downloaded on [Baidu Netdisk](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA),Extraction code:frgi.
+
+If you want to finetune on the basis of the existing model effect, please refer to the following instructions to modify the configuration file:
+
+Take `rec_french_lite_train` as an example:
+
+```
+Global:
+ ...
+ # Add a custom dictionary, if you modify the dictionary
+ # please point the path to the new dictionary
+ character_dict_path: ./ppocr/utils/dict/french_dict.txt
+ # Add data augmentation during training
+ distort: true
+ # Identify spaces
+ use_space_char: true
+ ...
+ # Modify reader type
+ reader_yml: ./configs/rec/multi_languages/rec_french_reader.yml
+ ...
+...
+```
+
### EVALUATION
The evaluation data set can be modified via `configs/rec/rec_icdar15_reader.yml` setting of `label_file_path` in EvalReader.
@@ -210,11 +279,13 @@ The evaluation data set can be modified via `configs/rec/rec_icdar15_reader.yml`
```
export CUDA_VISIBLE_DEVICES=0
# GPU evaluation, Global.checkpoints is the weight to be tested
-python3 tools/eval.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
+python3 tools/eval.py -c configs/rec/rec_icdar15_reader.yml -o Global.checkpoints={path/to/weights}/best_accuracy
```
+
### PREDICTION
+
* Training engine prediction
Using the model trained by paddleocr, you can quickly get prediction through the following script.
@@ -223,7 +294,7 @@ The default prediction picture is stored in `infer_img`, and the weight is speci
```
# Predict English results
-python3 tools/infer_rec.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/en/word_1.jpg
+python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/en/word_1.jpg
```
Input image:
@@ -238,11 +309,11 @@ infer_img: doc/imgs_words/en/word_1.png
word : joint
```
-The configuration file used for prediction must be consistent with the training. For example, you completed the training of the Chinese model with `python3 tools/train.py -c configs/rec/rec_chinese_lite_train.yml`, you can use the following command to predict the Chinese model:
+The configuration file used for prediction must be consistent with the training. For example, you completed the training of the Chinese model with `python3 tools/train.py -c configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml`, you can use the following command to predict the Chinese model:
```
# Predict Chinese results
-python3 tools/infer_rec.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/ch/word_1.jpg
+python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/ch/word_1.jpg
```
Input image:
diff --git a/doc/doc_en/serving_en.md b/doc/doc_en/serving_en.md
deleted file mode 100644
index 7439cc84abb58f091febc3acda169816d34a836b..0000000000000000000000000000000000000000
--- a/doc/doc_en/serving_en.md
+++ /dev/null
@@ -1,187 +0,0 @@
-# Service deployment
-
-PaddleOCR provides 2 service deployment methods:：
-- Based on **HubServing**：Has been integrated into PaddleOCR ([code](https://github.com/PaddlePaddle/PaddleOCR/tree/develop/deploy/hubserving)). Please follow this tutorial.
-- Based on **PaddleServing**：See PaddleServing official website for details ([demo](https://github.com/PaddlePaddle/Serving/tree/develop/python/examples/ocr)). Follow-up will also be integrated into PaddleOCR.
-
-The service deployment directory includes three service packages: detection, recognition, and two-stage series connection. Select the corresponding service package to install and start service according to your needs. The directory is as follows:
-```
-deploy/hubserving/
- └─ ocr_det detection module service package
- └─ ocr_rec recognition module service package
- └─ ocr_system two-stage series connection service package
-```
-
-Each service pack contains 3 files. Take the 2-stage series connection service package as an example, the directory is as follows:
-```
-deploy/hubserving/ocr_system/
- └─ __init__.py Empty file, required
- └─ config.json Configuration file, optional, passed in as a parameter when using configuration to start the service
- └─ module.py Main module file, required, contains the complete logic of the service
- └─ params.py Parameter file, required, including parameters such as model path, pre- and post-processing parameters
-```
-
-## Quick start service
-The following steps take the 2-stage series service as an example. If only the detection service or recognition service is needed, replace the corresponding file path.
-
-### 1. Prepare the environment
-```shell
-# Install paddlehub
-pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
-
-# Set environment variables on Linux
-export PYTHONPATH=.
-# Set environment variables on Windows
-SET PYTHONPATH=.
-```
-
-### 2. Install Service Module
-PaddleOCR provides 3 kinds of service modules, install the required modules according to your needs.
-
-* On Linux platform, the examples are as follows.
-```shell
-# Install the detection service module:
-hub install deploy/hubserving/ocr_det/
-
-# Or, install the recognition service module:
-hub install deploy/hubserving/ocr_rec/
-
-# Or, install the 2-stage series service module:
-hub install deploy/hubserving/ocr_system/
-```
-
-* On Windows platform, the examples are as follows.
-```shell
-# Install the detection service module:
-hub install deploy\hubserving\ocr_det\
-
-# Or, install the recognition service module:
-hub install deploy\hubserving\ocr_rec\
-
-# Or, install the 2-stage series service module:
-hub install deploy\hubserving\ocr_system\
-```
-
-### 3. Start service
-#### Way 1. Start with command line parameters (CPU only)
-
-**start command：**
-```shell
-$ hub serving start --modules [Module1==Version1, Module2==Version2, ...] \
- --port XXXX \
- --use_multiprocess \
- --workers \
-```
-**parameters：**
-
-|parameters|usage|
-|-|-|
-|--modules/-m|PaddleHub Serving pre-installed model, listed in the form of multiple Module==Version key-value pairs
*`When Version is not specified, the latest version is selected by default`*|
-|--port/-p|Service port, default is 8866|
-|--use_multiprocess|Enable concurrent mode, the default is single-process mode, this mode is recommended for multi-core CPU machines
*`Windows operating system only supports single-process mode`*|
-|--workers|The number of concurrent tasks specified in concurrent mode, the default is `2*cpu_count-1`, where `cpu_count` is the number of CPU cores|
-
-For example, start the 2-stage series service:
-```shell
-hub serving start -m ocr_system
-```
-
-This completes the deployment of a service API, using the default port number 8866.
-
-#### Way 2. Start with configuration file（CPU、GPU）
-**start command：**
-```shell
-hub serving start --config/-c config.json
-```
-Wherein, the format of `config.json` is as follows:
-```python
-{
- "modules_info": {
- "ocr_system": {
- "init_args": {
- "version": "1.0.0",
- "use_gpu": true
- },
- "predict_args": {
- }
- }
- },
- "port": 8868,
- "use_multiprocess": false,
- "workers": 2
-}
-```
-- The configurable parameters in `init_args` are consistent with the `_initialize` function interface in `module.py`. Among them, **when `use_gpu` is `true`, it means that the GPU is used to start the service**.
-- The configurable parameters in `predict_args` are consistent with the `predict` function interface in `module.py`.
-
-**Note:**
-- When using the configuration file to start the service, other parameters will be ignored.
-- If you use GPU prediction (that is, `use_gpu` is set to `true`), you need to set the environment variable CUDA_VISIBLE_DEVICES before starting the service, such as: ```export CUDA_VISIBLE_DEVICES=0```, otherwise you do not need to set it.
-- **`use_gpu` and `use_multiprocess` cannot be `true` at the same time.**
-
-For example, use GPU card No. 3 to start the 2-stage series service:
-```shell
-export CUDA_VISIBLE_DEVICES=3
-hub serving start -c deploy/hubserving/ocr_system/config.json
-```
-
-## Send prediction requests
-After the service starts, you can use the following command to send a prediction request to obtain the prediction result:
-```shell
-python tools/test_hubserving.py server_url image_path
-```
-
-Two parameters need to be passed to the script:
-- **server_url**：service address，format of which is
-`http://[ip_address]:[port]/predict/[module_name]`
-For example, if the detection, recognition and 2-stage serial services are started with provided configuration files, the respective `server_url` would be:
-`http://127.0.0.1:8866/predict/ocr_det`
-`http://127.0.0.1:8867/predict/ocr_rec`
-`http://127.0.0.1:8868/predict/ocr_system`
-- **image_path**：Test image path, can be a single image path or an image directory path
-
-**Eg.**
-```shell
-python tools/test_hubserving.py http://127.0.0.1:8868/predict/ocr_system ./doc/imgs/
-```
-
-## Returned result format
-The returned result is a list. Each item in the list is a dict. The dict may contain three fields. The information is as follows:
-
-|field name|data type|description|
-|-|-|-|
-|text|str|text content|
-|confidence|float|text recognition confidence|
-|text_region|list|text location coordinates|
-
-The fields returned by different modules are different. For example, the results returned by the text recognition service module do not contain `text_region`. The details are as follows:
-
-|field name/module name|ocr_det|ocr_rec|ocr_system|
-|-|-|-|-|
-|text||✔|✔|
-|confidence||✔|✔|
-|text_region|✔||✔|
-
-**Note：** If you need to add, delete or modify the returned fields, you can modify the file `module.py` of the corresponding module. For the complete process, refer to the user-defined modification service module in the next section.
-
-## User defined service module modification
-If you need to modify the service logic, the following steps are generally required (take the modification of `ocr_system` for example):
-
-- 1. Stop service
-```shell
-hub serving stop --port/-p XXXX
-```
-- 2. Modify the code in the corresponding files, like `module.py` and `params.py`, according to the actual needs.
-For example, if you need to replace the model used by the deployed service, you need to modify model path parameters `det_model_dir` and `rec_model_dir` in `params.py`. Of course, other related parameters may need to be modified at the same time. Please modify and debug according to the actual situation. It is suggested to run `module.py` directly for debugging after modification before starting the service test.
-- 3. Uninstall old service module
-```shell
-hub uninstall ocr_system
-```
-- 4. Install modified service module
-```shell
-hub install deploy/hubserving/ocr_system/
-```
-- 5. Restart service
-```shell
-hub serving start -m ocr_system
-```
diff --git a/doc/doc_en/tree_en.md b/doc/doc_en/tree_en.md
new file mode 100644
index 0000000000000000000000000000000000000000..8f05de7e76b243bdcc6358bfea4e153e769dd3d5
--- /dev/null
+++ b/doc/doc_en/tree_en.md
@@ -0,0 +1,208 @@
+# Overall directory structure
+
+The overall directory structure of PaddleOCR is introduced as follows:
+
+```
+PaddleOCR
+├── configs // configuration file, you can select model structure and modify hyperparameters through yml file
+│ ├── cls // Related configuration files of direction classifier
+│ │ ├── cls_mv3.yml // training configuration related, including backbone network, head, loss, optimizer
+│ │ └── cls_reader.yml // Data reading related, data reading method, data storage path
+│ ├── det // Detection related configuration files
+│ │ ├── det_db_icdar15_reader.yml // data read
+│ │ ├── det_mv3_db.yml // training configuration
+│ │ ...
+│ └── rec // Identify related configuration files
+│ ├── rec_benchmark_reader.yml // LMDB format data reading related
+│ ├── rec_chinese_common_train.yml // General Chinese training configuration
+│ ├── rec_icdar15_reader.yml // simple data reading related, including data reading function, data path, label file
+│ ...
+├── deploy // deployment related
+│ ├── android_demo // android_demo
+│ │ ...
+│ ├── cpp_infer // C++ infer
+│ │ ├── CMakeLists.txt // Cmake file
+│ │ ├── docs // documentation
+│ │ │ └── windows_vs2019_build.md
+│ │ ├── include
+│ │ │ ├── clipper.h // clipper library
+│ │ │ ├── config.h // infer configuration
+│ │ │ ├── ocr_cls.h // direction classifier
+│ │ │ ├── ocr_det.h // text detection
+│ │ │ ├── ocr_rec.h // text recognition
+│ │ │ ├── postprocess_op.h // postprocess after detection
+│ │ │ ├── preprocess_op.h // preprocess detection
+│ │ │ └── utility.h // tools
+│ │ ├── readme.md // documentation
+│ │ ├── ...
+│ │ ├── src // source file
+│ │ │ ├── clipper.cpp
+│ │ │ ├── config.cpp
+│ │ │ ├── main.cpp
+│ │ │ ├── ocr_cls.cpp
+│ │ │ ├── ocr_det.cpp
+│ │ │ ├── ocr_rec.cpp
+│ │ │ ├── postprocess_op.cpp
+│ │ │ ├── preprocess_op.cpp
+│ │ │ └── utility.cpp
+│ │ └── tools // compile and execute script
+│ │ ├── build.sh // compile script
+│ │ ├── config.txt // configuration file
+│ │ └── run.sh // Test startup script
+│ ├── docker
+│ │ └── hubserving
+│ │ ├── cpu
+│ │ │ └── Dockerfile
+│ │ ├── gpu
+│ │ │ └── Dockerfile
+│ │ ├── README_cn.md
+│ │ ├── README.md
+│ │ └── sample_request.txt
+│ ├── hubserving // hubserving
+│ │ ├── ocr_det // text detection
+│ │ │ ├── config.json // serving configuration
+│ │ │ ├── __init__.py
+│ │ │ ├── module.py // prediction model
+│ │ │ └── params.py // prediction parameters
+│ │ ├── ocr_rec // text recognition
+│ │ │ ├── config.json
+│ │ │ ├── __init__.py
+│ │ │ ├── module.py
+│ │ │ └── params.py
+│ │ └── ocr_system // system forecast
+│ │ ├── config.json
+│ │ ├── __init__.py
+│ │ ├── module.py
+│ │ └── params.py
+│ ├── imgs // prediction picture
+│ │ ├── cpp_infer_pred_12.png
+│ │ └── demo.png
+│ ├── ios_demo // ios demo
+│ │ ...
+│ ├── lite // lite deployment
+│ │ ├── cls_process.cc // direction classifier data processing
+│ │ ├── cls_process.h
+│ │ ├── config.txt // check configuration parameters
+│ │ ├── crnn_process.cc // crnn data processing
+│ │ ├── crnn_process.h
+│ │ ├── db_post_process.cc // db data processing
+│ │ ├── db_post_process.h
+│ │ ├── Makefile // compile file
+│ │ ├── ocr_db_crnn.cc // series prediction
+│ │ ├── prepare.sh // data preparation
+│ │ ├── readme.md // documentation
+│ │ ...
+│ ├── pdserving // pdserving deployment
+│ │ ├── det_local_server.py // fast detection version, easy deployment and fast prediction
+│ │ ├── det_web_server.py // Full version of detection, high stability and distributed deployment
+│ │ ├── ocr_local_server.py // detection + identification quick version
+│ │ ├── ocr_web_client.py // client
+│ │ ├── ocr_web_server.py // detection + identification full version
+│ │ ├── readme.md // documentation
+│ │ ├── rec_local_server.py // recognize quick version
+│ │ └── rec_web_server.py // Identify the full version
+│ └── slim
+│ └── quantization // quantization related
+│ ├── export_model.py // export model
+│ ├── quant.py // quantization
+│ └── README.md // Documentation
+├── doc // Documentation tutorial
+│ ...
+├── paddleocr.py
+├── ppocr // network core code
+│ ├── data // data processing
+│ │ ├── cls // direction classifier
+│ │ │ ├── dataset_traversal.py // Data transmission, define data reader, read data and form batch
+│ │ │ └── randaugment.py // Random data augmentation operation
+│ │ ├── det // detection
+│ │ │ ├── data_augment.py // data augmentation operation
+│ │ │ ├── dataset_traversal.py // Data transmission, define data reader, read data and form batch
+│ │ │ ├── db_process.py // db data processing
+│ │ │ ├── east_process.py // east data processing
+│ │ │ ├── make_border_map.py // Generate boundary map
+│ │ │ ├── make_shrink_map.py // Generate shrink map
+│ │ │ ├── random_crop_data.py // random crop
+│ │ │ └── sast_process.py // sast data processing
+│ │ ├── reader_main.py // main function of data reader
+│ │ └── rec // recognation
+│ │ ├── dataset_traversal.py // Data transmission, define data reader, including LMDB_Reader and Simple_Reader
+│ │ └── img_tools.py // Data processing related, including data normalization and disturbance
+│ ├── __init__.py
+│ ├── modeling // networking related
+│ │ ├── architectures // Model architecture, which defines the various modules required by the model
+│ │ │ ├── cls_model.py // direction classifier
+│ │ │ ├── det_model.py // detection
+│ │ │ └── rec_model.py // recognition
+│ │ ├── backbones // backbone network
+│ │ │ ├── det_mobilenet_v3.py // detect mobilenet_v3
+│ │ │ ├── det_resnet_vd.py
+│ │ │ ├── det_resnet_vd_sast.py
+│ │ │ ├── rec_mobilenet_v3.py // recognize mobilenet_v3
+│ │ │ ├── rec_resnet_fpn.py
+│ │ │ └── rec_resnet_vd.py
+│ │ ├── common_functions.py // common functions
+│ │ ├── heads
+│ │ │ ├── cls_head.py // class header
+│ │ │ ├── det_db_head.py // db detection head
+│ │ │ ├── det_east_head.py // east detection head
+│ │ │ ├── det_sast_head.py // sast detection head
+│ │ │ ├── rec_attention_head.py // recognition attention
+│ │ │ ├── rec_ctc_head.py // recognition ctc
+│ │ │ ├── rec_seq_encoder.py // recognition sequence code
+│ │ │ ├── rec_srn_all_head.py // srn related
+│ │ │ └── self_attention // srn attention
+│ │ │ └── model.py
+│ │ ├── losses // loss function
+│ │ │ ├── cls_loss.py // Directional classifier loss function
+│ │ │ ├── det_basic_loss.py // detect basic loss
+│ │ │ ├── det_db_loss.py // DB loss
+│ │ │ ├── det_east_loss.py // EAST loss
+│ │ │ ├── det_sast_loss.py // SAST loss
+│ │ │ ├── rec_attention_loss.py // attention loss
+│ │ │ ├── rec_ctc_loss.py // ctc loss
+│ │ │ └── rec_srn_loss.py // srn loss
+│ │ └── stns // Spatial transformation network
+│ │ └── tps.py // TPS conversion
+│ ├── optimizer.py // optimizer
+│ ├── postprocess // post-processing
+│ │ ├── db_postprocess.py // DB postprocess
+│ │ ├── east_postprocess.py // East postprocess
+│ │ ├── lanms // lanms related
+│ │ │ ...
+│ │ ├── locality_aware_nms.py // nms
+│ │ └── sast_postprocess.py // sast post-processing
+│ └── utils // tools
+│ ├── character.py // Character processing, including text encoding and decoding, and calculation of prediction accuracy
+│ ├── check.py // parameter loading check
+│ ├── ic15_dict.txt // English number dictionary, case sensitive
+│ ├── ppocr_keys_v1.txt // Chinese dictionary, used to train Chinese models
+│ ├── save_load.py // model save and load function
+│ ├── stats.py // Statistics
+│ └── utility.py // Tool functions, including related check tools such as whether the input parameters are legal
+├── README_en.md // documentation
+├── README.md
+├── requirments.txt // installation dependencies
+├── setup.py // whl package packaging script
+└── tools // start tool
+ ├── eval.py // evaluation function
+ ├── eval_utils // evaluation tools
+ │ ├── eval_cls_utils.py // category related
+ │ ├── eval_det_iou.py // detect iou related
+ │ ├── eval_det_utils.py // detection related
+ │ ├── eval_rec_utils.py // recognition related
+ │ └── __init__.py
+ ├── export_model.py // export infer model
+ ├── infer // Forecast based on prediction engine
+ │ ├── predict_cls.py
+ │ ├── predict_det.py
+ │ ├── predict_rec.py
+ │ ├── predict_system.py
+ │ └── utility.py
+ ├── infer_cls.py // Predict classification based on training engine
+ ├── infer_det.py // Predictive detection based on training engine
+ ├── infer_rec.py // Predictive recognition based on training engine
+ ├── program.py // overall process
+ ├── test_hubserving.py
+ └── train.py // start training
+
+```
diff --git a/doc/doc_en/update_en.md b/doc/doc_en/update_en.md
index ca050370989ba3cded8c7211b7ab297ebe239c5f..71f784812bcac9ff55aa0523831ba9b1a5849403 100644
--- a/doc/doc_en/update_en.md
+++ b/doc/doc_en/update_en.md
@@ -1,4 +1,8 @@
# RECENT UPDATES
+- 2020.9.22 Update the PP-OCR technical article, https://arxiv.org/abs/2009.09941
+- 2020.9.19 Update the ultra lightweight compressed ppocr_mobile_slim series models, the overall model size is 3.5M (see [PP-OCR Pipline](../../README.md#PP-OCR-Pipline)), suitable for mobile deployment. [Model Downloads](../../README.md#Supported-Chinese-model-list)
+- 2020.9.17 Update the ultra lightweight ppocr_mobile series and general ppocr_server series Chinese and English ocr models, which are comparable to commercial effects. [Model Downloads](../../README.md#Supported-Chinese-model-list)
+- 2020.9.17 update [English recognition model](./models_list_en.md#english-recognition-model) and [Multilingual recognition model](./models_list_en.md#english-recognition-model), `German`, `French`, `Japanese` and `Korean` have been supported. Models for more languages will continue to be updated.
- 2020.8.24 Support the use of PaddleOCR through whl package installation，pelease refer [PaddleOCR Package](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/whl_en.md)
- 2020.8.16 Release text detection algorithm [SAST](https://arxiv.org/abs/1908.05498) and text recognition algorithm [SRN](https://arxiv.org/abs/2003.12294)
- 2020.7.23, Release the playback and PPT of live class on BiliBili station, PaddleOCR Introduction, [address](https://aistudio.baidu.com/aistudio/course/introduce/1519)
@@ -7,7 +11,7 @@
- 2020.7.15, Add several related datasets, data annotation and synthesis tools.
- 2020.7.9 Add a new model to support recognize the character "space".
- 2020.7.9 Add the data augument and learning rate decay strategies during training.
-- 2020.6.8 Add [datasets](./doc/doc_en/datasets_en.md) and keep updating
+- 2020.6.8 Add [datasets](./datasets_en.md) and keep updating
- 2020.6.5 Support exporting `attention` model to `inference_model`
- 2020.6.5 Support separate prediction and recognition, output result score
- 2020.6.5 Support exporting `attention` model to `inference_model`
diff --git a/doc/doc_en/visualization_en.md b/doc/doc_en/visualization_en.md
index 698db3f2d8ae31dd14829f7a1dfbd13f174f3e23..2b88b1de6042b6f63cefb85134f58d83d4c625f8 100644
--- a/doc/doc_en/visualization_en.md
+++ b/doc/doc_en/visualization_en.md
@@ -1,71 +1,49 @@
-# Visualization
-
-- [Chinese/English OCR Visualization (Space_support )](#Space_support)
-- [Ultra-lightweight Chinese/English OCR Visualization](#Ultra-lightweight)
-- [General Chinese/English OCR Visualization](#General)
-
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-
-## Chinese/English OCR Visualization (Space_support )
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-### Ultra-lightweight Model
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