+
+
+
+ + +------------------------------------------------------------------------------------------ + +
+ 
+ 
+ 
+ 
+
-
+
+
+ + +------------------------------------------------------------------------------------------ + +
+
+
+
+
-上图是通用ppocr_server模型效果展示，更多效果图请见[效果展示页面](./doc/doc_ch/visualization.md)。
+上图是通用PP-OCR server模型效果展示，更多效果图请见[效果展示页面](./doc/doc_ch/visualization.md)。
## 欢迎加入PaddleOCR技术交流群
@@ -62,71 +77,78 @@ PaddleOCR同时支持动态图与静态图两种编程范式
- 代码体验：从[快速安装](./doc/doc_ch/quickstart.md) 开始
-## PP-OCR 2.0系列模型列表（更新中）
-**说明** ：2.0版模型和[1.1版模型](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/models_list.md)的主要区别在于动态图训练vs.静态图训练，模型性能上无明显差距。
+## PP-OCR系列模型列表（更新中）
+
| 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（9.4M） | ch_ppocr_mobile_v2.0_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) |
-| 中英文通用OCR模型（143.4M） |ch_ppocr_server_v2.0_xx|服务器端 |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) |
+| 中英文超轻量PP-OCRv2模型（11.6M） | ch_ppocrv2_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_det_distill_train.tar)| [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_rec_train.tar)|
+| 中英文超轻量PP-OCR mobile模型（9.4M） | ch_ppocr_mobile_v2.0_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) |
+| 中英文通用PP-OCR server模型（143.4M） |ch_ppocr_server_v2.0_xx|服务器端 |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) |
-更多模型下载（包括多语言），可以参考[PP-OCR v2.0 系列模型下载](./doc/doc_ch/models_list.md)
+更多模型下载（包括多语言），可以参考[PP-OCR 系列模型下载](./doc/doc_ch/models_list.md)
## 文档教程
-- [快速安装](./doc/doc_ch/installation.md)
-- [中文OCR模型快速使用](./doc/doc_ch/quickstart.md)
-- [多语言OCR模型快速使用](./doc/doc_ch/multi_languages.md)
-- [代码组织结构](./doc/doc_ch/tree.md)
-- 算法介绍
- - [文本检测](./doc/doc_ch/algorithm_overview.md)
- - [文本识别](./doc/doc_ch/algorithm_overview.md)
- - [PP-OCR Pipeline](#PP-OCR)
- - [端到端PGNet算法](./doc/doc_ch/pgnet.md)
-- 模型训练/评估
- - [文本检测](./doc/doc_ch/detection.md)
- - [文本识别](./doc/doc_ch/recognition.md)
- - [方向分类器](./doc/doc_ch/angle_class.md)
- - [yml参数配置文件介绍](./doc/doc_ch/config.md)
-- 预测部署
- - [基于pip安装whl包快速推理](./doc/doc_ch/whl.md)
- - [基于Python脚本预测引擎推理](./doc/doc_ch/inference.md)
- - [基于C++预测引擎推理](./deploy/cpp_infer/readme.md)
- - [服务化部署](./deploy/pdserving/README_CN.md)
- - [端侧部署](./deploy/lite/readme.md)
- - [Benchmark](./doc/doc_ch/benchmark.md)
-- 数据集
- - [通用中英文OCR数据集](./doc/doc_ch/datasets.md)
- - [手写中文OCR数据集](./doc/doc_ch/handwritten_datasets.md)
- - [垂类多语言OCR数据集](./doc/doc_ch/vertical_and_multilingual_datasets.md)
+- [运行环境准备](./doc/doc_ch/environment.md)
+- [快速开始（中英文/多语言/文档分析）](./doc/doc_ch/quickstart.md)
+- [PaddleOCR全景图与项目克隆](./doc/doc_ch/paddleOCR_overview.md)
+- PP-OCR产业落地：从训练到部署
+ - [PP-OCR模型与配置文件](./doc/doc_ch/models_and_config.md)
+ - [PP-OCR模型下载](./doc/doc_ch/models_list.md)
+ - [配置文件内容与生成](./doc/doc_ch/config.md)
+ - [模型库快速使用](./doc/doc_ch/inference.md)
+ - [PP-OCR模型训练](./doc/doc_ch/training.md)
+ - [文本检测](./doc/doc_ch/detection.md)
+ - [文本识别](./doc/doc_ch/recognition.md)
+ - [方向分类器](./doc/doc_ch/angle_class.md)
+ - PP-OCR模型推理部署
+ - [基于Python脚本预测引擎推理](./doc/doc_ch/inference.md)
+ - [基于C++预测引擎推理](./deploy/cpp_infer/readme.md)
+ - [服务化部署](./deploy/pdserving/README_CN.md)
+ - [端侧部署](./deploy/lite/readme.md)
+ - [Benchmark](./doc/doc_ch/benchmark.md)
+- [PP-Structure信息提取](./ppstructure/README_ch.md)
+ - [版面分析](./ppstructure/layout/README_ch.md)
+ - [表格识别](./ppstructure/table/README_ch.md)
- 数据标注与合成
- [半自动标注工具PPOCRLabel](./PPOCRLabel/README_ch.md)
- [数据合成工具Style-Text](./StyleText/README_ch.md)
- [其它数据标注工具](./doc/doc_ch/data_annotation.md)
- [其它数据合成工具](./doc/doc_ch/data_synthesis.md)
+- OCR学术圈
+ - [两阶段模型介绍与下载](./doc/doc_ch/algorithm_overview.md)
+ - [端到端PGNet算法](./doc/doc_ch/pgnet.md)
+- 数据集
+ - [通用中英文OCR数据集](./doc/doc_ch/datasets.md)
+ - [手写中文OCR数据集](./doc/doc_ch/handwritten_datasets.md)
+ - [垂类多语言OCR数据集](./doc/doc_ch/vertical_and_multilingual_datasets.md)
- [效果展示](#效果展示)
- FAQ
- [【精选】OCR精选10个问题](./doc/doc_ch/FAQ.md)
- - [【理论篇】OCR通用32个问题](./doc/doc_ch/FAQ.md)
- - [【实战篇】PaddleOCR实战110个问题](./doc/doc_ch/FAQ.md)
+ - [【理论篇】OCR通用50个问题](./doc/doc_ch/FAQ.md)
+ - [【实战篇】PaddleOCR实战183个问题](./doc/doc_ch/FAQ.md)
- [技术交流群](#欢迎加入PaddleOCR技术交流群)
- [参考文献](./doc/doc_ch/reference.md)
- [许可证书](#许可证书)
- [贡献代码](#贡献代码)
+- [代码组织结构](./doc/doc_ch/tree.md)
+
+
-
-## PP-OCR Pipeline
+## PP-OCRv2 Pipeline
+
- 
+ 
+
+
将下载到的数据集解压到工作目录下，假设解压在 PaddleOCR/train_data/ 下。另外，PaddleOCR将零散的标注文件整理成单独的标注文件
，您可以通过wget的方式进行下载。
```shell
@@ -23,7 +45,7 @@ python gen_label.py --mode="det" --root_path="/path/to/icdar_c4_train_imgs/" \
--output_label="/path/to/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：
+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，按照如下方式组织icdar2015数据集：
```
/PaddleOCR/train_data/icdar2015/text_localization/
└─ icdar_c4_train_imgs/ icdar数据集的训练数据
@@ -42,11 +64,13 @@ json.dumps编码前的图像标注信息是包含多个字典的list，字典中
如果您想在其他数据集上训练，可以按照上述形式构建标注文件。
-## 快速启动训练
+
+## 1.2 下载预训练模型
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/develop/ppcls/modeling/architectures)中的模型更换backbone，
-对应的backbone预训练模型可以从[PaddleClas repo 主页中找到下载链接](https://github.com/PaddlePaddle/PaddleClas#mobile-series)。
+您可以根据需求使用[PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.0/ppcls/modeling/architectures)中的模型更换backbone，
+对应的backbone预训练模型可以从[PaddleClas repo 主页中找到下载链接](https://github.com/PaddlePaddle/PaddleClas/blob/release%2F2.0/README_cn.md#resnet%E5%8F%8A%E5%85%B6vd%E7%B3%BB%E5%88%97)。
+
```shell
cd PaddleOCR/
# 根据backbone的不同选择下载对应的预训练模型
@@ -56,23 +80,23 @@ wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dyg
wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_vd_pretrained.pdparams
# 或，下载ResNet50_vd的预训练模型
wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams
-
```
-#### 启动训练
+
+## 1.3 启动训练
*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
# 单机单卡训练 mv3_db 模型
python3 tools/train.py -c configs/det/det_mv3_db.yml \
- -o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained/
+ -o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained
+
# 单机多卡训练，通过 --gpus 参数设置使用的GPU ID
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml \
- -o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained/
+ -o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained
```
-
上述指令中，通过-c 选择训练使用configs/det/det_db_mv3.yml配置文件。
有关配置文件的详细解释，请参考[链接](./config.md)。
@@ -81,46 +105,122 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/
python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
```
-#### 断点训练
+
+## 1.4 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
+```
+**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。
+
+
+## 1.5 更换Backbone 训练
+
+PaddleOCR将网络划分为四部分，分别在[ppocr/modeling](../../ppocr/modeling)下。 进入网络的数据将按照顺序(transforms->backbones->
+necks->heads)依次通过这四个部分。
+```bash
+├── architectures # 网络的组网代码
+├── transforms # 网络的图像变换模块
+├── backbones # 网络的特征提取模块
+├── necks # 网络的特征增强模块
+└── heads # 网络的输出模块
```
+如果要更换的Backbone 在PaddleOCR中有对应实现，直接修改配置yml文件中`Backbone`部分的参数即可。
-**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。
+如果要使用新的Backbone，更换backbones的例子如下:
+
+1. 在 [ppocr/modeling/backbones](../../ppocr/modeling/backbones) 文件夹下新建文件，如my_backbone.py。
+2. 在 my_backbone.py 文件内添加相关代码，示例代码如下:
+
+```python
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+
+class MyBackbone(nn.Layer):
+ def __init__(self, *args, **kwargs):
+ super(MyBackbone, self).__init__()
+ # your init code
+ self.conv = nn.xxxx
+
+ def forward(self, inputs):
+ # your network forward
+ y = self.conv(inputs)
+ return y
+```
+
+3. 在 [ppocr/modeling/backbones/\__init\__.py](../../ppocr/modeling/backbones/__init__.py)文件内导入添加的`MyBackbone`模块，然后修改配置文件中Backbone进行配置即可使用，格式如下:
-## 指标评估
+```yaml
+Backbone:
+name: MyBackbone
+args1: args1
+```
-PaddleOCR计算三个OCR检测相关的指标，分别是：Precision、Recall、Hmean。
+**注意**：如果要更换网络的其他模块，可以参考[文档](./add_new_algorithm.md)。
-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。
+
+## 1.6 指标评估
+
+PaddleOCR计算三个OCR检测相关的指标，分别是：Precision、Recall、Hmean（F-Score）。
-评估时设置后处理参数`box_thresh=0.5`，`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
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。
+
```shell
-python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="{path/to/weights}/best_accuracy" PostProcess.box_thresh=0.5 PostProcess.unclip_ratio=1.5
+python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="{path/to/weights}/best_accuracy"
```
-
* 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
-## 测试检测效果
+
+## 1.7 测试检测效果
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 Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy"
```
-测试DB模型时，调整后处理阈值，
+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 Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./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.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=2.0
```
-
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 Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy"
```
+
+
+## 1.8 转inference模型测试
+
+inference 模型（`paddle.jit.save`保存的模型）
+一般是模型训练，把模型结构和模型参数保存在文件中的固化模型，多用于预测部署场景。
+训练过程中保存的模型是checkpoints模型，保存的只有模型的参数，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，适合于实际系统集成。
+
+检测模型转inference 模型方式：
+```shell
+# 加载配置文件`det_mv3_db.yml`，从`output/det_db`目录下加载`best_accuracy`模型，inference模型保存在`./output/det_db_inference`目录下
+python3 tools/export_model.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model="./output/det_db/best_accuracy" Global.save_inference_dir="./output/det_db_inference/"
+```
+
+DB检测模型inference 模型预测：
+```shell
+python3 tools/infer/predict_det.py --det_algorithm="DB" --det_model_dir="./output/det_db_inference/" --image_dir="./doc/imgs/" --use_gpu=True
+```
+如果是其他检测，比如EAST模型，det_algorithm参数需要修改为EAST，默认为DB算法：
+```shell
+python3 tools/infer/predict_det.py --det_algorithm="EAST" --det_model_dir="./output/det_db_inference/" --image_dir="./doc/imgs/" --use_gpu=True
+```
+
+
+# 2. FAQ
+
+Q1: 训练模型转inference 模型之后预测效果不一致？
+**A**：此类问题出现较多，问题多是trained model预测时候的预处理、后处理参数和inference model预测的时候的预处理、后处理参数不一致导致的。以det_mv3_db.yml配置文件训练的模型为例，训练模型、inference模型预测结果不一致问题解决方式如下：
+- 检查[trained model预处理](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/configs/det/det_mv3_db.yml#L116)，和[inference model的预测预处理](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/tools/infer/predict_det.py#L42)函数是否一致。算法在评估的时候，输入图像大小会影响精度，为了和论文保持一致，训练icdar15配置文件中将图像resize到[736, 1280]，但是在inference model预测的时候只有一套默认参数，会考虑到预测速度问题，默认限制图像最长边为960做resize的。训练模型预处理和inference模型的预处理函数位于[ppocr/data/imaug/operators.py](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/ppocr/data/imaug/operators.py#L147)
+- 检查[trained model后处理](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/configs/det/det_mv3_db.yml#L51)，和[inference 后处理参数](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/tools/infer/utility.py#L50)是否一致。
diff --git a/doc/doc_ch/environment.md b/doc/doc_ch/environment.md
new file mode 100644
index 0000000000000000000000000000000000000000..8efc31983a9d7cee50f922a3f84a2b1a2de23889
--- /dev/null
+++ b/doc/doc_ch/environment.md
@@ -0,0 +1,327 @@
+# 运行环境准备
+
+* [1. Python环境搭建](#1)
+ + [1.1 Windows](#1.1)
+ + [1.2 Mac](#1.2)
+ + [1.3 Linux](#1.3)
+* [2. 安装PaddlePaddle](#2)
+
+
+
+## 1. Python环境搭建
+
+
+
+### 1.1 Windows
+
+#### 1.1.1 安装Anaconda
+
+- 说明：使用paddlepaddle需要先安装python环境，这里我们选择python集成环境Anaconda工具包
+ - Anaconda是1个常用的python包管理程序
+ - 安装完Anaconda后，可以安装python环境，以及numpy等所需的工具包环境。
+- Anaconda下载：
+ - 地址：https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/?C=M&O=D
+ - 大部分win10电脑均为64位操作系统，选择x86_64版本；若电脑为32位操作系统，则选择x86.exe
+
+ 
+ - 下载完成后，双击安装程序进入图形界面
+ - 默认安装位置为C盘，建议将安装位置更改到D盘：
+
+ 
+ - 勾选conda加入环境变量，忽略警告：
+
+ 
+
+#### 1.1.2 打开终端并创建conda环境
+
+- 打开Anaconda Prompt终端：左下角Windows Start Menu -> Anaconda3 -> Anaconda Prompt启动控制台
+
+ 
+
+
+- 创建新的conda环境
+
+ ```shell
+ # 在命令行输入以下命令，创建名为paddle_env的环境
+ # 此处为加速下载，使用清华源
+ conda create --name paddle_env python=3.8 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ # 这是一行命令
+ ```
+
+ 该命令会创建1个名为paddle_env、python版本为3.8的可执行环境，根据网络状态，需要花费一段时间
+
+ 之后命令行中会输出提示信息，输入y并回车继续安装
+
+ 
+
+- 激活刚创建的conda环境，在命令行中输入以下命令：
+
+ ```shell
+ # 激活paddle_env环境
+ conda activate paddle_env
+ # 查看当前python的位置
+ where python
+ ```
+
+ 
+
+
+
+
+以上anaconda环境和python环境安装完毕
+
+
+
+### 1.2 Mac
+
+#### 1.2.1 安装Anaconda
+
+- 说明：使用paddlepaddle需要先安装python环境，这里我们选择python集成环境Anaconda工具包
+ - Anaconda是1个常用的python包管理程序
+ - 安装完Anaconda后，可以安装python环境，以及numpy等所需的工具包环境
+- Anaconda下载：
+ - 地址：https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/?C=M&O=D
+
+ 
+
+ - 选择最下方的`Anaconda3-2021.05-MacOSX-x86_64.pkg`下载
+- 下载完成后，双击.pkg文件进入图形界面
+ - 按默认设置即可，安装需要花费一段时间
+- 建议安装vscode或pycharm等代码编辑器
+
+#### 1.2.2 打开终端并创建conda环境
+
+- 打开终端
+
+ - 同时按下command键和空格键，在聚焦搜索中输入"终端"，双击进入终端
+
+- **将conda加入环境变量**
+
+ - 加入环境变量是为了让系统能识别conda命令
+
+ - 输入以下命令，在终端中打开`~/.bash_profile`：
+
+ ```shell
+ vim ~/.bash_profile
+ ```
+
+ - 在`~/.bash_profile`中将conda添加为环境变量：
+
+ ```shell
+ # 先按i进入编辑模式
+ # 在第一行输入：
+ export PATH="~/opt/anaconda3/bin:$PATH"
+ # 若安装时自定义了安装位置，则将~/opt/anaconda3/bin改为自定义的安装目录下的bin文件夹
+ ```
+
+ ```shell
+ # 修改后的~/.bash_profile文件应如下（其中xxx为用户名）：
+ export PATH="~/opt/anaconda3/bin:$PATH"
+ # >>> conda initialize >>>
+ # !! Contents within this block are managed by 'conda init' !!
+ __conda_setup="$('/Users/xxx/opt/anaconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)"
+ if [ $? -eq 0 ]; then
+ eval "$__conda_setup"
+ else
+ if [ -f "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh" ]; then
+ . "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh"
+ else
+ export PATH="/Users/xxx/opt/anaconda3/bin:$PATH"
+ fi
+ fi
+ unset __conda_setup
+ # <<< conda initialize <<<
+ ```
+
+ - 修改完成后，先按`esc`键退出编辑模式，再输入`:wq!`并回车，以保存退出
+
+ - 验证是否能识别conda命令：
+
+ - 在终端中输入`source ~/.bash_profile`以更新环境变量
+ - 再在终端输入`conda info --envs`，若能显示当前有base环境，则conda已加入环境变量
+
+- 创建新的conda环境
+
+ ```shell
+ # 在命令行输入以下命令，创建名为paddle_env的环境
+ # 此处为加速下载，使用清华源
+ conda create --name paddle_env python=3.8 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
+ ```
+
+ - 该命令会创建1个名为paddle_env、python版本为3.8的可执行环境，根据网络状态，需要花费一段时间
+
+ - 之后命令行中会输出提示信息，输入y并回车继续安装
+
+ - 
+
+- 激活刚创建的conda环境，在命令行中输入以下命令：
+
+ ```shell
+ # 激活paddle_env环境
+ conda activate paddle_env
+ # 查看当前python的位置
+ where python
+ ```
+
+ 
+
+以上anaconda环境和python环境安装完毕
+
+
+
+### 1.3 Linux
+
+Linux用户可选择Anaconda或Docker两种方式运行。如果你熟悉Docker且需要训练PaddleOCR模型，推荐使用Docker环境，PaddleOCR的开发流程均在Docker环境下运行。如果你不熟悉Docker，也可以使用Anaconda来运行项目。
+
+#### 1.3.1 Anaconda环境配置
+
+- 说明：使用paddlepaddle需要先安装python环境，这里我们选择python集成环境Anaconda工具包
+ - Anaconda是1个常用的python包管理程序
+ - 安装完Anaconda后，可以安装python环境，以及numpy等所需的工具包环境
+
+- **下载Anaconda**：
+
+ - 下载地址：https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/?C=M&O=D
+ 
+
+ - 选择适合您操作系统的版本
+ - 可在终端输入`uname -m`查询系统所用的指令集
+
+- 下载法1：本地下载，再将安装包传到linux服务器上
+
+- 下载法2：直接使用linux命令行下载
+
+ ```shell
+ # 首先安装wget
+ sudo apt-get install wget # Ubuntu
+ sudo yum install wget # CentOS
+ ```
+
+ ```shell
+ # 然后使用wget从清华源上下载
+ # 如要下载Anaconda3-2021.05-Linux-x86_64.sh，则下载命令如下：
+ wget https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda3-2021.05-Linux-x86_64.sh
+
+ # 若您要下载其他版本，需要将最后1个/后的文件名改成您希望下载的版本
+ ```
+
+- 安装Anaconda：
+
+ - 在命令行输入`sh Anaconda3-2021.05-Linux-x86_64.sh`
+ - 若您下载的是其它版本，则将该命令的文件名替换为您下载的文件名
+ - 按照安装提示安装即可
+ - 查看许可时可输入q来退出
+
+- **将conda加入环境变量**
+
+ - 加入环境变量是为了让系统能识别conda命令，若您在安装时已将conda加入环境变量path，则可跳过本步
+
+ - 在终端中打开`~/.bashrc`：
+
+ ```shell
+ # 在终端中输入以下命令：
+ vim ~/.bashrc
+ ```
+
+ - 在`~/.bashrc`中将conda添加为环境变量：
+
+ ```shell
+ # 先按i进入编辑模式
+ # 在第一行输入：
+ export PATH="~/anaconda3/bin:$PATH"
+ # 若安装时自定义了安装位置，则将~/anaconda3/bin改为自定义的安装目录下的bin文件夹
+ ```
+
+ ```shell
+ # 修改后的~/.bash_profile文件应如下（其中xxx为用户名）：
+ export PATH="~/opt/anaconda3/bin:$PATH"
+ # >>> conda initialize >>>
+ # !! Contents within this block are managed by 'conda init' !!
+ __conda_setup="$('/Users/xxx/opt/anaconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)"
+ if [ $? -eq 0 ]; then
+ eval "$__conda_setup"
+ else
+ if [ -f "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh" ]; then
+ . "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh"
+ else
+ export PATH="/Users/xxx/opt/anaconda3/bin:$PATH"
+ fi
+ fi
+ unset __conda_setup
+ # <<< conda initialize <<<
+ ```
+
+ - 修改完成后，先按`esc`键退出编辑模式，再输入`:wq!`并回车，以保存退出
+
+ - 验证是否能识别conda命令：
+
+ - 在终端中输入`source ~/.bash_profile`以更新环境变量
+ - 再在终端输入`conda info --envs`，若能显示当前有base环境，则conda已加入环境变量
+
+- 创建新的conda环境
+
+ ```shell
+ # 在命令行输入以下命令，创建名为paddle_env的环境
+ # 此处为加速下载，使用清华源
+ conda create --name paddle_env python=3.8 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
+ ```
+
+ - 该命令会创建1个名为paddle_env、python版本为3.8的可执行环境，根据网络状态，需要花费一段时间
+
+ - 之后命令行中会输出提示信息，输入y并回车继续安装
+
+ 
+
+- 激活刚创建的conda环境，在命令行中输入以下命令：
+
+ ```shell
+ # 激活paddle_env环境
+ conda activate paddle_env
+ ```
+
+
+以上anaconda环境和python环境安装完毕
+
+#### 1.3.2 Docker环境配置
+
+**注意：第一次使用这个镜像，会自动下载该镜像，请耐心等待。**
+
+```bash
+# 切换到工作目录下
+cd /home/Projects
+# 首次运行需创建一个docker容器，再次运行时不需要运行当前命令
+# 创建一个名字为ppocr的docker容器，并将当前目录映射到容器的/paddle目录下
+
+如果您希望在CPU环境下使用docker，使用docker而不是nvidia-docker创建docker
+sudo docker run --name ppocr -v $PWD:/paddle --network=host -it paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82 /bin/bash
+
+如果使用CUDA10，请运行以下命令创建容器，设置docker容器共享内存shm-size为64G，建议设置32G以上
+sudo nvidia-docker run --name ppocr -v $PWD:/paddle --shm-size=64G --network=host -it paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82 /bin/bash
+
+您也可以访问[DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/)获取与您机器适配的镜像。
+
+# ctrl+P+Q可退出docker 容器，重新进入docker 容器使用如下命令
+sudo docker container exec -it ppocr /bin/bash
+```
+
+
+
+## 2. 安装PaddlePaddle
+
+- 如果您的机器安装的是CUDA9或CUDA10，请运行以下命令安装
+
+```bash
+python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
+```
+
+- 如果您的机器是CPU，请运行以下命令安装
+
+```bash
+python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
+```
+
+更多的版本需求，请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
+
+
+
+
+
diff --git a/doc/doc_ch/inference_ppocr.md b/doc/doc_ch/inference_ppocr.md
new file mode 100644
index 0000000000000000000000000000000000000000..493a4c9868621b762895e1ee11f76ac250918453
--- /dev/null
+++ b/doc/doc_ch/inference_ppocr.md
@@ -0,0 +1,136 @@
+# PP-OCR模型库快速推理
+
+本文介绍针对PP-OCR模型库的Python推理引擎使用方法，内容依次为文本检测、文本识别、方向分类器以及三者串联在CPU、GPU上的预测方法。
+
+
+- [1. 文本检测模型推理](#文本检测模型推理)
+
+- [2. 文本识别模型推理](#文本识别模型推理)
+ - [2.1 超轻量中文识别模型推理](#超轻量中文识别模型推理)
+ - [2.2 多语言模型的推理](#多语言模型的推理)
+
+- [3. 方向分类模型推理](#方向分类模型推理)
+
+- [4. 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. 文本检测模型推理
+
+文本检测模型推理，默认使用DB模型的配置参数。超轻量中文检测模型推理，可以执行如下命令：
+
+```
+# 下载超轻量中文检测模型：
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tartar xf ch_ppocr_mobile_v2.0_det_infer.tarpython3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_ppocr_mobile_v2.0_det_infer/"
+```
+
+可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'det_res'。结果示例如下：
+
+
+
+通过参数`limit_type`和`det_limit_side_len`来对图片的尺寸进行限制，
+`limit_type`可选参数为[`max`, `min`]，
+`det_limit_size_len` 为正整数，一般设置为32 的倍数，比如960。
+
+参数默认设置为`limit_type='max', det_limit_side_len=960`。表示网络输入图像的最长边不能超过960，
+如果超过这个值，会对图像做等宽比的resize操作，确保最长边为`det_limit_side_len`。
+设置为`limit_type='min', det_limit_side_len=960` 则表示限制图像的最短边为960。
+
+如果输入图片的分辨率比较大，而且想使用更大的分辨率预测，可以设置det_limit_side_len 为想要的值，比如1216：
+
+```
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1216
+```
+
+如果想使用CPU进行预测，执行命令如下
+
+```
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False
+```
+
+
+
+
+
+## 2. 文本识别模型推理
+
+
+
+### 2.1 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：
+
+```
+# 下载超轻量中文识别模型：
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
+tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="ch_ppocr_mobile_v2.0_rec_infer"
+```
+
+
+
+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：
+
+```bash
+Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.98458153)
+```
+
+
+
+### 2.2 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/fonts/` 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/fonts/korean.ttf"
+```
+
+
+
+执行命令后，上图的预测结果为：
+
+``` text
+Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904)
+```
+
+
+
+## 3. 方向分类模型推理
+
+方向分类模型推理，可以执行如下命令：
+
+```
+# 下载超轻量中文方向分类器模型：
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
+tar xf ch_ppocr_mobile_v2.0_cls_infer.tar
+python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --cls_model_dir="ch_ppocr_mobile_v2.0_cls_infer"
+```
+
+
+
+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', 0.9999982]
+```
+
+
+
+## 4. 文本检测、方向分类和文字识别串联推理
+
+以超轻量中文OCR模型推理为例，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。`use_mp`表示是否使用多进程。`total_process_num`表示在使用多进程时的进程数。可视化识别结果默认保存到 ./inference_results 文件夹里面。
+
+```shell
+# 使用方向分类器
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.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/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false
+# 使用多进程
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false --use_mp=True --total_process_num=6
+```
+
+执行命令后，识别结果图像如下：
+
+
+
diff --git a/doc/doc_ch/models_and_config.md b/doc/doc_ch/models_and_config.md
new file mode 100644
index 0000000000000000000000000000000000000000..cc425947238e12961d84be3878e178135b996c43
--- /dev/null
+++ b/doc/doc_ch/models_and_config.md
@@ -0,0 +1,47 @@
+
+# PP-OCR模型与配置文件
+PP-OCR模型与配置文件一章主要补充一些OCR模型的基本概念、配置文件的内容与作用以便对模型后续的参数调整和训练中拥有更好的体验。
+
+本节包含三个部分，首先在[PP-OCR模型下载](./models_list.md)中解释PP-OCR模型的类型概念，并提供所有模型的下载链接。然后在[配置文件内容与生成](./config.md)中详细说明调整PP-OCR模型所需的参数。最后的[模型库快速使用](./inference.md)是对第一节PP-OCR模型库使用方法的介绍，可以通过Python推理引擎快速利用丰富的模型库模型获得测试结果。
+
+------
+
+下面我们首先了解一些OCR相关的基本概念：
+
+- [1. OCR 简要介绍](#1-ocr-----)
+ * [1.1 OCR 检测模型基本概念](#11-ocr---------)
+ * [1.2 OCR 识别模型基本概念](#12-ocr---------)
+ * [1.3 PP-OCR模型](#13-pp-ocr--)
+
+
+## 1. OCR 简要介绍
+本节简要介绍OCR检测模型、识别模型的基本概念，并介绍PaddleOCR的PP-OCR模型。
+
+OCR（Optical Character Recognition，光学字符识别）目前是文字识别的统称，已不限于文档或书本文字识别，更包括识别自然场景下的文字，又可以称为STR（Scene Text Recognition）。
+
+OCR文字识别一般包括两个部分，文本检测和文本识别；文本检测首先利用检测算法检测到图像中的文本行；然后检测到的文本行用识别算法去识别到具体文字。
+
+
+### 1.1 OCR 检测模型基本概念
+
+文本检测就是要定位图像中的文字区域，然后通常以边界框的形式将单词或文本行标记出来。传统的文字检测算法多是通过手工提取特征的方式，特点是速度快，简单场景效果好，但是面对自然场景，效果会大打折扣。当前多是采用深度学习方法来做。
+
+基于深度学习的文本检测算法可以大致分为以下几类：
+1. 基于目标检测的方法；一般是预测得到文本框后，通过NMS筛选得到最终文本框，多是四点文本框，对弯曲文本场景效果不理想。典型算法为EAST、Text Box等方法。
+2. 基于分割的方法；将文本行当成分割目标，然后通过分割结果构建外接文本框，可以处理弯曲文本，对于文本交叉场景问题效果不理想。典型算法为DB、PSENet等方法。
+3. 混合目标检测和分割的方法；
+
+
+### 1.2 OCR 识别模型基本概念
+
+OCR识别算法的输入数据一般是文本行，背景信息不多，文字占据主要部分，识别算法目前可以分为两类算法：
+1. 基于CTC的方法；即识别算法的文字预测模块是基于CTC的，常用的算法组合为CNN+RNN+CTC。目前也有一些算法尝试在网络中加入transformer模块等等。
+2. 基于Attention的方法；即识别算法的文字预测模块是基于Attention的，常用算法组合是CNN+RNN+Attention。
+
+
+### 1.3 PP-OCR模型
+
+PaddleOCR 中集成了很多OCR算法，文本检测算法有DB、EAST、SAST等等，文本识别算法有CRNN、RARE、StarNet、Rosetta、SRN等算法。
+
+其中PaddleOCR针对中英文自然场景通用OCR，推出了PP-OCR系列模型，PP-OCR模型由DB+CRNN算法组成，利用海量中文数据训练加上模型调优方法，在中文场景上具备较高的文本检测识别能力。并且PaddleOCR推出了高精度超轻量PP-OCRv2模型，检测模型仅3M，识别模型仅8.5M，利用[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)的模型量化方法，可以在保持精度不降低的情况下，将检测模型压缩到0.8M，识别压缩到3M，更加适用于移动端部署场景。
+
diff --git a/doc/doc_ch/models_list.md b/doc/doc_ch/models_list.md
index 35713ae67f797618e043697eb93642208c3df865..59a36b578ef1ad99ae62c4a09db78fb4562538eb 100644
--- a/doc/doc_ch/models_list.md
+++ b/doc/doc_ch/models_list.md
@@ -1,8 +1,9 @@
-## OCR模型列表（V2.0，2021年1月20日更新）
+## OCR模型列表（V2.1，2021年9月6日更新）
> **说明**
-> 1. 2.0版模型和[1.1版模型](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/models_list.md) 的主要区别在于动态图训练vs.静态图训练，模型性能上无明显差距。
-> 2. 本文档提供的是PPOCR自研模型列表，更多基于公开数据集的算法介绍与预训练模型可以参考：[算法概览文档](./algorithm_overview.md)。
+> 1. 2.1版模型相比2.0版模型，2.1的模型在模型精度上做了提升
+> 2. 2.0版模型和[1.1版模型](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/models_list.md) 的主要区别在于动态图训练vs.静态图训练，模型性能上无明显差距。
+> 3. 本文档提供的是PPOCR自研模型列表，更多基于公开数据集的算法介绍与预训练模型可以参考：[算法概览文档](./algorithm_overview.md)。
- [一、文本检测模型](#文本检测模型)
@@ -32,6 +33,8 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
|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_v2.1_det|slim量化+蒸馏版超轻量模型，支持中英文、多语种文本检测|[ch_det_lite_train_cml_v2.1.yml](../../configs/det/ch_ppocr_v2.1/ch_det_lite_train_cml_v2.1.yml)| 3M |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_det_slim_quant_infer.tar)|
+|ch_ppocr_mobile_v2.1_det|原始超轻量模型，支持中英文、多语种文本检测|[ch_det_lite_train_cml_v2.1.ym](../../configs/det/ch_ppocr_v2.1/ch_det_lite_train_cml_v2.1.yml)|3M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_det_distill_train.tar)|
|ch_ppocr_mobile_slim_v2.0_det|slim裁剪版超轻量模型，支持中英文、多语种文本检测|[ch_det_mv3_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml)| 2.6M |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar)|
|ch_ppocr_mobile_v2.0_det|原始超轻量模型，支持中英文、多语种文本检测|[ch_det_mv3_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml)|3M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|
|ch_ppocr_server_v2.0_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|[ch_det_res18_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml)|47M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar)|
@@ -45,6 +48,8 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
|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_v2.1_rec|slim量化版超轻量模型，支持中英文、数字识别|[rec_chinese_lite_train_distillation_v2.1.yml](../../configs/rec/ch_ppocr_v2.1/rec_chinese_lite_train_distillation_v2.1.yml)| 9M |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_rec_slim_quant_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_rec_slim_quant_train.tar) |
+|ch_ppocr_mobile_v2.1_rec|原始超轻量模型，支持中英文、数字识别|[rec_chinese_lite_train_distillation_v2.1.yml](../../configs/rec/ch_ppocr_v2.1/rec_chinese_lite_train_distillation_v2.1.yml)|8.5M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_rec_train.tar) |
|ch_ppocr_mobile_slim_v2.0_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_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)| 6M |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_train.tar) |
|ch_ppocr_mobile_v2.0_rec|原始超轻量模型，支持中英文、数字识别|[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)|5.2M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) |
|ch_ppocr_server_v2.0_rec|通用模型，支持中英文、数字识别|[rec_chinese_common_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml)|94.8M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) |
@@ -62,46 +67,6 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
#### 3. 多语言识别模型（更多语言持续更新中...）
-**说明：** 新增的多语言模型的配置文件通过代码方式生成，您可以通过`--help`参数查看当前PaddleOCR支持生成哪些多语言的配置文件：
-```bash
-# 该代码需要在指定目录运行
-cd {your/path/}PaddleOCR/configs/rec/multi_language/
-python3 generate_multi_language_configs.py --help
-```
-下面以生成意大利语配置文件为例：
-##### 1. 生成意大利语配置文件测试现有模型
-
-如果您仅仅想用配置文件测试PaddleOCR提供的多语言模型可以通过下面命令生成默认的配置文件，使用PaddleOCR提供的小语种字典进行预测。
-```bash
-# 该代码需要在指定目录运行
-cd {your/path/}PaddleOCR/configs/rec/multi_language/
-# 通过-l或者--language参数设置需要生成的语种的配置文件，该命令会将默认参数写入配置文件
-python3 generate_multi_language_configs.py -l it
-```
-##### 2. 生成意大利语配置文件训练自己的数据
-如果您想训练自己的小语种模型，可以准备好训练集文件、验证集文件、字典文件和训练数据路径，这里假设准备的意大利语的训练集、验证集、字典和训练数据路径为：
-- 训练集:{your/path/}PaddleOCR/train_data/train_list.txt
-- 验证集:{your/path/}PaddleOCR/train_data/val_list.txt
-- 使用PaddleOCR提供的默认字典：{your/path/}PaddleOCR/ppocr/utils/dict/it_dict.txt
-- 训练数据路径:{your/path/}PaddleOCR/train_data
-
-使用以下命令生成配置文件：
-```bash
-# 该代码需要在指定目录运行
-cd {your/path/}PaddleOCR/configs/rec/multi_language/
-# -l或者--language字段是必须的
-# --train修改训练集，--val修改验证集，--data_dir修改数据集目录，-o修改对应默认参数
-# --dict命令改变字典路径，示例使用默认字典路径则该参数可不填
-python3 generate_multi_language_configs.py -l it \
---train train_data/train_list.txt \
---val train_data/val_list.txt \
---data_dir train_data \
--o Global.use_gpu=False
-```
-
-
-##### 3. 多语言模型与配置文件
-
|模型名称|字典文件|模型简介|配置文件|推理模型大小|下载地址|
| --- | --- | --- | --- |--- | --- |
| french_mobile_v2.0_rec | ppocr/utils/dict/french_dict.txt |法文识别|[rec_french_lite_train.yml](../../configs/rec/multi_language/rec_french_lite_train.yml)|2.65M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_train.tar) |
@@ -125,13 +90,15 @@ python3 generate_multi_language_configs.py -l it \
|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_v2.0_cls|slim量化版模型|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)| 2.1M |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) |
-|ch_ppocr_mobile_v2.0_cls|原始模型|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)|1.38M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |
+|ch_ppocr_mobile_slim_v2.0_cls|slim量化版模型，对检测到的文本行文字角度分类|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)| 2.1M |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) |
+|ch_ppocr_mobile_v2.0_cls|原始分类器模型，对检测到的文本行文字角度分类|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)|1.38M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |
### 四、Paddle-Lite 模型
|模型版本|模型简介|模型大小|检测模型|文本方向分类模型|识别模型|Paddle-Lite版本|
|---|---|---|---|---|---|---|
-|V2.0|超轻量中文OCR 移动端模型|7.8M|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_opt.nb)|v2.9|
-|V2.0(slim)|超轻量中文OCR 移动端模型|3.3M|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_slim_opt.nb)|v2.9|
+|V2.1|ppocr_v2.1蒸馏版超轻量中文OCR移动端模型|11M|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_det_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_rec_infer_opt.nb)|v2.9|
+|V2.1(slim)|ppocr_v2.1蒸馏版超轻量中文OCR移动端模型|4.9M|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_det_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_rec_slim_opt.nb)|v2.9|
+|V2.0|ppocr_v2.0超轻量中文OCR移动端模型|7.8M|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_opt.nb)|v2.9|
+|V2.0(slim)|ppocr_v2.0超轻量中文OCR移动端模型|3.3M|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_slim_opt.nb)|v2.9|
diff --git a/doc/doc_ch/multi_languages.md b/doc/doc_ch/multi_languages.md
index 1bebdb58fa025d5b6891fd1579285b5db956a2d4..af9ff82e357e5945bfddf10337d0af3cd04390a0 100644
--- a/doc/doc_ch/multi_languages.md
+++ b/doc/doc_ch/multi_languages.md
@@ -200,9 +200,9 @@ ppocr 支持使用自己的数据进行自定义训练或finetune, 其中识别
|英文|english|en| |乌克兰文|Ukranian|uk|
|法文|french|fr| |白俄罗斯文|Belarusian|be|
|德文|german|german| |泰卢固文|Telugu |te|
-|日文|japan|japan| | |阿巴扎文|Abaza |abq|
+|日文|japan|japan| | 阿巴扎文 | Abaza | abq |
|韩文|korean|korean| |泰米尔文|Tamil |ta|
-|中文繁体|chinese traditional |ch_tra| |南非荷兰文 |Afrikaans |af|
+|中文繁体|chinese traditional |chinese_cht| |南非荷兰文 |Afrikaans |af|
|意大利文| Italian |it| |阿塞拜疆文 |Azerbaijani |az|
|西班牙文|Spanish |es| |波斯尼亚文|Bosnian|bs|
|葡萄牙文| Portuguese|pt| |捷克文|Czech|cs|
diff --git a/doc/doc_ch/paddleOCR_overview.md b/doc/doc_ch/paddleOCR_overview.md
new file mode 100644
index 0000000000000000000000000000000000000000..f49c1ae302607ff6629da2462f91a36793b4db3a
--- /dev/null
+++ b/doc/doc_ch/paddleOCR_overview.md
@@ -0,0 +1,33 @@
+# PaddleOCR全景图与项目克隆
+
+## 1. PaddleOCR全景图
+
+PaddleOCR包含丰富的文本检测、文本识别以及端到端算法。结合实际测试与产业经验，PaddleOCR选择DB和CRNN作为基础的检测和识别模型，经过一系列优化策略提出面向产业应用的PP-OCR模型。PP-OCR模型针对通用场景，根据不同语种形成了PP-OCR模型库。基于PP-OCR的能力，PaddleOCR针对文档场景任务发布PP-Structure工具库，包含版面分析和表格识别两大任务。为了打通产业落地的全流程，PaddleOCR提供了规模化的数据生产工具和多种预测部署工具，助力开发者快速落地。
+
+
+
+ 
+
+
+ 
+ 
+
-## 通用ppocr_server_2.0 效果展示
+## 通用PP-OCR server 效果展示
@@ -10,8 +16,6 @@
- 
- 
+
+
+
Decompress the downloaded dataset to the working directory, assuming it is decompressed under PaddleOCR/train_data/. In addition, PaddleOCR organizes many scattered annotation files into two separate annotation files for train and test respectively, which can be downloaded by wget:
```shell
# Under the PaddleOCR path
@@ -36,10 +58,11 @@ The `points` in the dictionary represent the coordinates (x, y) of the four poin
If you want to train PaddleOCR on other datasets, please build the annotation file according to the above format.
-## TRAINING
+## 1.2 DOWNLOAD PRETRAINED MODEL
+
+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/release/2.0/ppcls/modeling/architectures) to replace backbone according to your needs.
+And the responding download link of backbone pretrain weights can be found in (https://github.com/PaddlePaddle/PaddleClas/blob/release%2F2.0/README_cn.md#resnet%E5%8F%8A%E5%85%B6vd%E7%B3%BB%E5%88%97).
-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/develop/ppcls/modeling/architectures) to replace backbone according to your needs.
-And the responding download link of backbone pretrain weights can be found in [PaddleClas repo](https://github.com/PaddlePaddle/PaddleClas#mobile-series).
```shell
cd PaddleOCR/
# Download the pre-trained model of MobileNetV3
@@ -49,11 +72,13 @@ wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dyg
# or, download the pre-trained model of ResNet50_vd
wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams
+```
-#### START TRAINING
+## 1.3 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.yml \
+ -o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained
```
In the above instruction, use `-c` to select the training to use the `configs/det/det_db_mv3.yml` configuration file.
@@ -62,16 +87,17 @@ 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
# single GPU training
-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.yml -o \
+ Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained \
+ Optimizer.base_lr=0.0001
# multi-GPU training
# Set the GPU ID used by the '--gpus' parameter.
-python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
-
+python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained
```
-#### load trained model and continue training
+## 1.4 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:
@@ -82,9 +108,59 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./you
**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.
-## EVALUATION
+## 1.5 TRAINING WITH NEW BACKBONE
+
+The network part completes the construction of the network, and PaddleOCR divides the network into four parts, which are under [ppocr/modeling](../../ppocr/modeling). The data entering the network will pass through these four parts in sequence(transforms->backbones->
+necks->heads).
+
+```bash
+├── architectures # Code for building network
+├── transforms # Image Transformation Module
+├── backbones # Feature extraction module
+├── necks # Feature enhancement module
+└── heads # Output module
+```
+
+If the Backbone to be replaced has a corresponding implementation in PaddleOCR, you can directly modify the parameters in the `Backbone` part of the configuration yml file.
+
+However, if you want to use a new Backbone, an example of replacing the backbones is as follows:
+
+1. Create a new file under the [ppocr/modeling/backbones](../../ppocr/modeling/backbones) folder, such as my_backbone.py.
+2. Add code in the my_backbone.py file, the sample code is as follows:
+
+```python
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+
+class MyBackbone(nn.Layer):
+ def __init__(self, *args, **kwargs):
+ super(MyBackbone, self).__init__()
+ # your init code
+ self.conv = nn.xxxx
+
+ def forward(self, inputs):
+ # your network forward
+ y = self.conv(inputs)
+ return y
+```
+
+3. Import the added module in the [ppocr/modeling/backbones/\__init\__.py](../../ppocr/modeling/backbones/__init__.py) file.
+
+After adding the four-part modules of the network, you only need to configure them in the configuration file to use, such as:
+
+```yaml
+ Backbone:
+ name: MyBackbone
+ args1: args1
+```
+
+**NOTE**: More details about replace Backbone and other mudule can be found in [doc](add_new_algorithm_en.md).
+
+## 1.6 EVALUATION
-PaddleOCR calculates three indicators for evaluating performance of OCR detection task: Precision, Recall, and Hmean.
+PaddleOCR calculates three indicators for evaluating performance of OCR detection task: Precision, Recall, and Hmean(F-Score).
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`
@@ -95,10 +171,9 @@ The model parameters during training are saved in the `Global.save_model_dir` di
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
```
+* Note: `box_thresh` and `unclip_ratio` are parameters required for DB post-processing, and not need to be set when evaluating the EAST and SAST model.
-* Note: `box_thresh` and `unclip_ratio` are parameters required for DB post-processing, and not need to be set when evaluating the EAST model.
-
-## TEST
+## 1.7 TEST
Test the detection result on a single image:
```shell
@@ -107,7 +182,7 @@ python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./
When testing the DB model, adjust the post-processing threshold:
```shell
-python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./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.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=2.0
```
@@ -115,3 +190,33 @@ Test the detection result on all images in the folder:
```shell
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy"
```
+
+## 1.8 INFERENCE MODEL PREDICTION
+
+The inference model (the model saved by `paddle.jit.save`) is generally a solidified model saved after the model training is completed, and is mostly used to give prediction in deployment.
+
+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. Therefore, it is easier to deploy because the model structure and model parameters are already solidified in the inference model file, and is suitable for integration with actual systems.
+
+Firstly, we can convert DB trained model to inference model:
+```shell
+python3 tools/export_model.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model="./output/det_db/best_accuracy" Global.save_inference_dir="./output/det_db_inference/"
+```
+
+The detection inference model prediction：
+```shell
+python3 tools/infer/predict_det.py --det_algorithm="DB" --det_model_dir="./output/det_db_inference/" --image_dir="./doc/imgs/" --use_gpu=True
+```
+
+If it is other detection algorithms, such as the EAST, the det_algorithm parameter needs to be modified to EAST, and the default is the DB algorithm:
+```shell
+python3 tools/infer/predict_det.py --det_algorithm="EAST" --det_model_dir="./output/det_db_inference/" --image_dir="./doc/imgs/" --use_gpu=True
+```
+
+# 2. FAQ
+
+Q1: The prediction results of trained model and inference model are inconsistent?
+**A**: Most of the problems are caused by the inconsistency of the pre-processing and post-processing parameters during the prediction of the trained model and the pre-processing and post-processing parameters during the prediction of the inference model. Taking the model trained by the det_mv3_db.yml configuration file as an example, the solution to the problem of inconsistent prediction results between the training model and the inference model is as follows:
+- Check whether the [trained model preprocessing](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/configs/det/det_mv3_db.yml#L116) is consistent with the prediction [preprocessing function of the inference model](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/tools/infer/predict_det.py#L42). When the algorithm is evaluated, the input image size will affect the accuracy. In order to be consistent with the paper, the image is resized to [736, 1280] in the training icdar15 configuration file, but there is only a set of default parameters when the inference model predicts, which will be considered To predict the speed problem, the longest side of the image is limited to 960 for resize by default. The preprocessing function of the training model preprocessing and the inference model is located in [ppocr/data/imaug/operators.py](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/ppocr/data/imaug/operators.py#L147)
+- Check whether the [post-processing of the trained model](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/configs/det/det_mv3_db.yml#L51) is consistent with the [post-processing parameters of the inference](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/tools/infer/utility.py#L50).
diff --git a/doc/doc_en/environment_en.md b/doc/doc_en/environment_en.md
new file mode 100644
index 0000000000000000000000000000000000000000..96a46cce3010934689e8d95985ca434f49d18886
--- /dev/null
+++ b/doc/doc_en/environment_en.md
@@ -0,0 +1,332 @@
+# Environment Preparation
+
+* [1. Python Environment Setup](#1)
+ + [1.1 Windows](#1.1)
+ + [1.2 Mac](#1.2)
+ + [1.3 Linux](#1.3)
+* [2. Install PaddlePaddle 2.0](#2)
+
+
+
+## 1. Python Environment Setup
+
+
+
+### 1.1 Windows
+
+#### 1.1.1 Install Anaconda
+
+- Note: To use paddlepaddle you need to install python environment first, here we choose python integrated environment Anaconda toolkit
+
+ - Anaconda is a common python package manager
+ - After installing Anaconda, you can install the python environment, as well as numpy and other required toolkit environment.
+
+- Anaconda download.
+
+ - Address: https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/?C=M&O=D
+
+ - Most Win10 computers are 64-bit operating systems, choose x86_64 version; if the computer is a 32-bit operating system, choose x86.exe
+
+
+
+ - After the download is complete, double-click the installer to enter the graphical interface
+
+ - The default installation location is C drive, it is recommended to change the installation location to D drive.
+
+
+
+ - Check conda to add environment variables and ignore the warning that
+
+
+
+
+#### 1.1.2 Opening the terminal and creating the conda environment
+
+- Open Anaconda Prompt terminal: bottom left Windows Start Menu -> Anaconda3 -> Anaconda Prompt start console
+
+
+
+
+- Create a new conda environment
+
+ ```shell
+ # Enter the following command at the command line to create an environment named paddle_env
+ # Here to speed up the download, use the Tsinghua source
+ conda create --name paddle_env python=3.8 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ # This is a one line command
+ ```
+
+ This command will create an executable environment named paddle_env with python version 3.8, which will take a while depending on the network status
+
+ The command line will then output a prompt, type y and enter to continue the installation
+
+
+
+- To activate the conda environment you just created, enter the following command at the command line.
+
+ ```shell
+ # Activate the paddle_env environment
+ conda activate paddle_env
+ # View the current location of python
+ where python
+ ```
+
+
+
+The above anaconda environment and python environment are installed
+
+
+
+
+
+### 1.2 Mac
+
+#### 1.2.1 Installing Anaconda
+
+- Note: To use paddlepaddle you need to install the python environment first, here we choose the python integrated environment Anaconda toolkit
+
+ - Anaconda is a common python package manager
+ - After installing Anaconda, you can install the python environment, as well as numpy and other required toolkit environment
+
+- Anaconda download:.
+
+ - Address: https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/?C=M&O=D
+
+
+
+ - Select `Anaconda3-2021.05-MacOSX-x86_64.pkg` at the bottom to download
+
+- After downloading, double click on the .pkg file to enter the graphical interface
+
+ - Just follow the default settings, it will take a while to install
+
+- It is recommended to install a code editor such as vscode or pycharm
+
+#### 1.2.2 Open a terminal and create a conda environment
+
+- Open the terminal
+
+ - Press command and spacebar at the same time, type "terminal" in the focus search, double click to enter terminal
+
+- **Add conda to the environment variables**
+
+ - Environment variables are added so that the system can recognize the conda command
+
+ - Open `~/.bash_profile` in the terminal by typing the following command.
+
+ ```shell
+ vim ~/.bash_profile
+ ```
+
+ - Add conda as an environment variable in `~/.bash_profile`.
+
+ ```shell
+ # Press i first to enter edit mode
+ # In the first line type.
+ export PATH="~/opt/anaconda3/bin:$PATH"
+ # If you customized the installation location during installation, change ~/opt/anaconda3/bin to the bin folder in the customized installation directory
+ ```
+
+ ```shell
+ # The modified ~/.bash_profile file should look like this (where xxx is the username)
+ export PATH="~/opt/anaconda3/bin:$PATH"
+ # >>> conda initialize >>>
+ # !!! Contents within this block are managed by 'conda init' !!!
+ __conda_setup="$('/Users/xxx/opt/anaconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)"
+ if [ $? -eq 0 ]; then
+ eval "$__conda_setup"
+ else
+ if [ -f "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh" ]; then
+ . "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh"
+ else
+ export PATH="/Users/xxx/opt/anaconda3/bin:$PATH"
+ fi
+ fi
+ unset __conda_setup
+ # <<< conda initialize <<<
+ ```
+
+ - When you are done, press `esc` to exit edit mode, then type `:wq!` and enter to save and exit
+
+ - Verify that the conda command is recognized.
+
+ - Enter `source ~/.bash_profile` in the terminal to update the environment variables
+ - Enter `conda info --envs` in the terminal again, if it shows that there is a base environment, then conda has been added to the environment variables
+
+- Create a new conda environment
+
+ ```shell
+ # Enter the following command at the command line to create an environment called paddle_env
+ # Here to speed up the download, use Tsinghua source
+ conda create --name paddle_env python=3.8 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
+ ```
+
+ - This command will create an executable environment named paddle_env with python version 3.8, which will take a while depending on the network status
+
+ - The command line will then output a prompt, type y and enter to continue the installation
+
+ -
+
+- To activate the conda environment you just created, enter the following command at the command line.
+
+ ```shell
+ # Activate the paddle_env environment
+ conda activate paddle_env
+ # View the current location of python
+ where python
+ ```
+
+
+
+The above anaconda environment and python environment are installed
+
+
+
+
+
+### 1.3 Linux
+
+Linux users can choose to run either Anaconda or Docker. If you are familiar with Docker and need to train the PaddleOCR model, it is recommended to use the Docker environment, where the development process of PaddleOCR is run. If you are not familiar with Docker, you can also use Anaconda to run the project.
+
+#### 1.3.1 Anaconda environment configuration
+
+- Note: To use paddlepaddle you need to install the python environment first, here we choose the python integrated environment Anaconda toolkit
+
+ - Anaconda is a common python package manager
+ - After installing Anaconda, you can install the python environment, as well as numpy and other required toolkit environment
+
+- **Download Anaconda**.
+
+ - Download at: https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/?C=M&O=D
+
+
+
+
+
+
+ - Select the appropriate version for your operating system
+ - Type `uname -m` in the terminal to check the command set used by your system
+
+ - Download method 1: Download locally, then transfer the installation package to the linux server
+
+ - Download method 2: Directly use linux command line to download
+
+ ```shell
+ # First install wget
+ sudo apt-get install wget # Ubuntu
+ sudo yum install wget # CentOS
+ ```
+ ```bash
+ # Then use wget to download from Tsinghua source
+ # If you want to download Anaconda3-2021.05-Linux-x86_64.sh, the download command is as follows
+ wget https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda3-2021.05-Linux-x86_64.sh
+ # If you want to download another version, you need to change the file name after the last 1 / to the version you want to download
+ ```
+
+- To install Anaconda.
+
+ - Type `sh Anaconda3-2021.05-Linux-x86_64.sh` at the command line
+ - If you downloaded a different version, replace the file name of the command with the name of the file you downloaded
+ - Just follow the installation instructions
+ - You can exit by typing q when viewing the license
+
+- **Add conda to the environment variables**
+
+ - If you have already added conda to the environment variable path during the installation, you can skip this step
+
+ - Open `~/.bashrc` in a terminal.
+
+ ```shell
+ # Enter the following command in the terminal.
+ vim ~/.bashrc
+ ```
+
+ - Add conda as an environment variable in `~/.bashrc`.
+
+ ```shell
+ # Press i first to enter edit mode # In the first line enter.
+ export PATH="~/anaconda3/bin:$PATH"
+ # If you customized the installation location during installation, change ~/anaconda3/bin to the bin folder in the customized installation directory
+ ```
+
+ ```shell
+ # The modified ~/.bash_profile file should look like this (where xxx is the username)
+ export PATH="~/opt/anaconda3/bin:$PATH"
+ # >>> conda initialize >>>
+ # !!! Contents within this block are managed by 'conda init' !!!
+ __conda_setup="$('/Users/xxx/opt/anaconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)"
+ if [ $? -eq 0 ]; then
+ eval "$__conda_setup"
+ else
+ if [ -f "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh" ]; then
+ . "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh"
+ else
+ export PATH="/Users/xxx/opt/anaconda3/bin:$PATH"
+ fi
+ fi
+ unset __conda_setup
+ # <<< conda initialize <<<
+ ```
+
+ - When you are done, press `esc` to exit edit mode, then type `:wq!` and enter to save and exit
+
+ - Verify that the conda command is recognized.
+
+ - Enter `source ~/.bash_profile` in the terminal to update the environment variables
+ - Enter `conda info --envs` in the terminal again, if it shows that there is a base environment, then conda has been added to the environment variables
+
+- Create a new conda environment
+
+ ```shell
+ # Enter the following command at the command line to create an environment called paddle_env
+ # Here to speed up the download, use Tsinghua source
+ conda create --name paddle_env python=3.8 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
+ ```
+
+ - This command will create an executable environment named paddle_env with python version 3.8, which will take a while depending on the network status
+
+ - The command line will then output a prompt, type y and enter to continue the installation
+
+
+
+- To activate the conda environment you just created, enter the following command at the command line.
+
+ ```shell
+ # Activate the paddle_env environment
+ conda activate paddle_env
+ ```
+
+The above anaconda environment and python environment are installed
+
+
+#### 1.3.2 Docker environment preparation
+
+**The first time you use this docker image, it will be downloaded automatically. Please be patient.**
+
+```bash
+# Switch to the working directory
+cd /home/Projects
+# You need to create a docker container for the first run, and do not need to run the current command when you run it again
+# Create a docker container named ppocr and map the current directory to the /paddle directory of the container
+
+# If using CPU, use docker instead of nvidia-docker to create docker
+sudo docker run --name ppocr -v $PWD:/paddle --network=host -it paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82 /bin/bash
+```
+
+
+
+## 2. Install PaddlePaddle 2.0
+
+- If you have cuda9 or cuda10 installed on your machine, please run the following command to install
+
+```bash
+python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
+```
+
+- If you only have cpu on your machine, please run the following command to install
+
+```bash
+python3 -m pip install paddlepaddle -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/inference_ppocr_en.md b/doc/doc_en/inference_ppocr_en.md
new file mode 100755
index 0000000000000000000000000000000000000000..5442d0c578027c33890fbb063a0d2c78ecd226c5
--- /dev/null
+++ b/doc/doc_en/inference_ppocr_en.md
@@ -0,0 +1,135 @@
+
+# Reasoning based on Python prediction engine
+
+This article introduces the use of the Python inference engine for the PP-OCR model library. The content is in order of text detection, text recognition, direction classifier and the prediction method of the three in series on the CPU and GPU.
+
+
+- [TEXT DETECTION MODEL INFERENCE](#DETECTION_MODEL_INFERENCE)
+
+- [TEXT RECOGNITION MODEL INFERENCE](#RECOGNITION_MODEL_INFERENCE)
+ - [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_RECOGNITION)
+ - [2. MULTILINGUAL MODEL INFERENCE](MULTILINGUAL_MODEL_INFERENCE)
+
+- [ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE)
+
+- [TEXT DETECTION ANGLE CLASSIFICATION AND RECOGNITION INFERENCE CONCATENATION](#CONCATENATION)
+
+
+
+## TEXT DETECTION MODEL INFERENCE
+
+The default configuration is based on the inference setting of the DB text detection model. For lightweight Chinese detection model inference, you can execute the following commands:
+
+```
+# download DB text detection inference model
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
+tar xf ch_ppocr_mobile_v2.0_det_infer.tar
+# predict
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/"
+```
+
+The visual text detection results are saved to the ./inference_results folder by default, and the name of the result file is prefixed with'det_res'. Examples of results are as follows:
+
+
+
+You can use the parameters `limit_type` and `det_limit_side_len` to limit the size of the input image,
+The optional parameters of `limit_type` are [`max`, `min`], and
+`det_limit_size_len` is a positive integer, generally set to a multiple of 32, such as 960.
+
+The default setting of the parameters is `limit_type='max', det_limit_side_len=960`. Indicates that the longest side of the network input image cannot exceed 960,
+If this value is exceeded, the image will be resized with the same width ratio to ensure that the longest side is `det_limit_side_len`.
+Set as `limit_type='min', det_limit_side_len=960`, it means that the shortest side of the image is limited to 960.
+
+If the resolution of the input picture is relatively large and you want to use a larger resolution prediction, you can set det_limit_side_len to the desired value, such as 1216:
+```
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1216
+```
+
+If you want to use the CPU for prediction, execute the command as follows
+```
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False
+```
+
+
+
+## TEXT RECOGNITION MODEL INFERENCE
+
+
+
+### 1. LIGHTWEIGHT CHINESE TEXT RECOGNITION MODEL REFERENCE
+
+For lightweight Chinese recognition model inference, you can execute the following commands:
+
+```
+# download CRNN text recognition inference model
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
+tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_10.png" --rec_model_dir="ch_ppocr_mobile_v2.0_rec_infer"
+```
+
+
+
+After executing the command, the prediction results (recognized text and score) of the above image will be printed on the screen.
+
+```bash
+Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.9897658)
+```
+
+
+
+### 2. 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/fonts` 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/fonts/korean.ttf"
+```
+
+
+After executing the command, the prediction result of the above figure is:
+
+``` text
+Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904)
+```
+
+
+
+## ANGLE CLASSIFICATION MODEL INFERENCE
+
+For angle classification model inference, you can execute the following commands:
+
+
+```
+# download text angle class inference model：
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
+tar xf ch_ppocr_mobile_v2.0_cls_infer.tar
+python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words_en/word_10.png" --cls_model_dir="ch_ppocr_mobile_v2.0_cls_infer"
+```
+
+
+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_en/word_10.png:['0', 0.9999995]
+```
+
+
+## TEXT DETECTION ANGLE CLASSIFICATION AND RECOGNITION INFERENCE CONCATENATION
+
+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 parameter `use_mp` specifies whether to use multi-process to infer `total_process_num` specifies process number when using multi-process. The parameter . The visualized recognition results are saved to the `./inference_results` folder by default.
+
+```shell
+# use direction classifier
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.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/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/"
+
+# use multi-process
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false --use_mp=True --total_process_num=6
+```
+
+
+After executing the command, the recognition result image is as follows:
+
+
diff --git a/doc/doc_en/models_and_config_en.md b/doc/doc_en/models_and_config_en.md
new file mode 100644
index 0000000000000000000000000000000000000000..f80c00715f148974db616f411740580c281659ed
--- /dev/null
+++ b/doc/doc_en/models_and_config_en.md
@@ -0,0 +1,40 @@
+# PP-OCR Model and Configuration
+- [INTRODUCTION ABOUT OCR](#introduction-about-ocr)
+ * [BASIC CONCEPTS OF OCR DETECTION MODEL](#basic-concepts-of-ocr-detection-model)
+ * [Basic concepts of OCR recognition model](#basic-concepts-of-ocr-recognition-model)
+ * [PP-OCR model](#pp-ocr-model)
+ * [And a table of contents](#and-a-table-of-contents)
+ * [On the right](#on-the-right)
+
+
+## 1. INTRODUCTION ABOUT OCR
+
+This section briefly introduces the basic concepts of OCR detection model and recognition model, and introduces PaddleOCR's PP-OCR model.
+
+OCR (Optical Character Recognition, Optical Character Recognition) is currently the general term for text recognition. It is not limited to document or book text recognition, but also includes recognizing text in natural scenes. It can also be called STR (Scene Text Recognition).
+
+OCR text recognition generally includes two parts, text detection and text recognition. The text detection module first uses detection algorithms to detect text lines in the image. And then the recognition algorithm to identify the specific text in the text line.
+
+
+### 1.1 BASIC CONCEPTS OF OCR DETECTION MODEL
+
+Text detection can locate the text area in the image, and then usually mark the word or text line in the form of a bounding box. Traditional text detection algorithms mostly extract features manually, which are characterized by fast speed and good effect in simple scenes, but the effect will be greatly reduced when faced with natural scenes. Currently, deep learning methods are mostly used.
+
+Text detection algorithms based on deep learning can be roughly divided into the following categories:
+1. Method based on target detection. Generally, after the text box is predicted, the final text box is filtered through NMS, which is mostly four-point text box, which is not ideal for curved text scenes. Typical algorithms are methods such as EAST and Text Box.
+2. Method based on text segmentation. The text line is regarded as the segmentation target, and then the external text box is constructed through the segmentation result, which can handle curved text, and the effect is not ideal for the text cross scene problem. Typical algorithms are DB, PSENet and other methods.
+3. Hybrid target detection and segmentation method.
+
+
+### 1.2 Basic concepts of OCR recognition model
+
+The input of the OCR recognition algorithm is generally text lines images which has less background information, and the text information occupies the main part. The recognition algorithm can be divided into two types of algorithms:
+1. CTC-based method. The text prediction module of the recognition algorithm is based on CTC, and the commonly used algorithm combination is CNN+RNN+CTC. There are also some algorithms that try to add transformer modules to the network and so on.
+2. Attention-based method. The text prediction module of the recognition algorithm is based on Attention, and the commonly used algorithm combination is CNN+RNN+Attention.
+
+
+### 1.3 PP-OCR model
+
+PaddleOCR integrates many OCR algorithms, text detection algorithms include DB, EAST, SAST, etc., text recognition algorithms include CRNN, RARE, StarNet, Rosetta, SRN and other algorithms.
+
+Among them, PaddleOCR has released the PP-OCR series model for the general OCR in Chinese and English natural scenes. The PP-OCR model is composed of the DB+CRNN algorithm. It uses massive Chinese data training and model tuning methods to have high text detection and recognition capabilities in Chinese scenes. And PaddleOCR has launched a high-precision and ultra-lightweight PP-OCRv2 model. The detection model is only 3M, and the recognition model is only 8.5M. Using [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)'s model quantification method, the detection model can be compressed to 0.8M without reducing the accuracy. The recognition is compressed to 3M, which is more suitable for mobile deployment scenarios.
diff --git a/doc/doc_en/models_list_en.md b/doc/doc_en/models_list_en.md
index 9bee4aef5121b1964a9bdbdeeaad4e81dd9ff6d4..d699678e64963d8949c559e422f48b814ec3b921 100644
--- a/doc/doc_en/models_list_en.md
+++ b/doc/doc_en/models_list_en.md
@@ -1,7 +1,8 @@
-## OCR model list（V2.0, updated on 2021.1.20）
+## OCR model list（V2.1, updated on 2021.9.6）
> **Note**
-> 1. Compared with [models 1.1](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/models_list_en.md), which are trained with static graph programming paradigm, models 2.0 are the dynamic graph trained version and achieve close performance.
-> 2. All models in this tutorial are all ppocr-series models, for more introduction of algorithms and models based on public dataset, you can refer to [algorithm overview tutorial](./algorithm_overview_en.md).
+> 1. Compared with the model v2.0, the 2.1 version of the detection model has a improvement in accuracy, and the 2.1 version of the recognition model is optimized in accuracy and CPU speed.
+> 2. Compared with [models 1.1](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/models_list_en.md), which are trained with static graph programming paradigm, models 2.0 are the dynamic graph trained version and achieve close performance.
+> 3. All models in this tutorial are all ppocr-series models, for more introduction of algorithms and models based on public dataset, you can refer to [algorithm overview tutorial](./algorithm_overview_en.md).
- [1. Text Detection Model](#Detection)
- [2. Text Recognition Model](#Recognition)
@@ -28,6 +29,8 @@ Relationship of the above models is as follows.
|model name|description|config|model size|download|
| --- | --- | --- | --- | --- |
+|ch_ppocr_mobile_slim_v2.1_det|slim quantization with distillation lightweight model, supporting Chinese, English, multilingual text detection|[ch_det_lite_train_cml_v2.1.yml](../../configs/det/ch_ppocr_v2.1/ch_det_lite_train_cml_v2.1.yml)| 3M |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_det_slim_quant_infer.tar)|
+|ch_ppocr_mobile_v2.1_det|Original lightweight model, supporting Chinese, English, multilingual text detection|[ch_det_lite_train_cml_v2.1.ym](../../configs/det/ch_ppocr_v2.1/ch_det_lite_train_cml_v2.1.yml)|3M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_det_distill_train.tar)|
|ch_ppocr_mobile_slim_v2.0_det|Slim pruned lightweight model, supporting Chinese, English, multilingual text detection|[ch_det_mv3_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml)|2.6M |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar)|
|ch_ppocr_mobile_v2.0_det|Original lightweight model, supporting Chinese, English, multilingual text detection|[ch_det_mv3_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml)|3M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|
|ch_ppocr_server_v2.0_det|General model, which is larger than the lightweight model, but achieved better performance|[ch_det_res18_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml)|47M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar)|
@@ -40,6 +43,8 @@ Relationship of the above models is as follows.
|model name|description|config|model size|download|
| --- | --- | --- | --- | --- |
+|ch_ppocr_mobile_slim_v2.1_rec|Slim qunatization with distillation lightweight model, supporting Chinese, English, multilingual text detection|[rec_chinese_lite_train_distillation_v2.1.yml](../../configs/rec/ch_ppocr_v2.1/rec_chinese_lite_train_distillation_v2.1.yml)| 9M |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_rec_slim_quant_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_rec_slim_quant_train.tar) |
+|ch_ppocr_mobile_v2.1_rec|Original lightweight model, supporting Chinese, English, multilingual text detection|[rec_chinese_lite_train_distillation_v2.1.yml](../../configs/rec/ch_ppocr_v2.1/rec_chinese_lite_train_distillation_v2.1.yml)|8.5M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_rec_train.tar) |
|ch_ppocr_mobile_slim_v2.0_rec|Slim pruned and quantized lightweight model, supporting Chinese, English and number recognition|[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)| 6M | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_train.tar) |
|ch_ppocr_mobile_v2.0_rec|Original lightweight model, supporting Chinese, English and number recognition|[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)|5.2M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) |
|ch_ppocr_server_v2.0_rec|General model, supporting Chinese, English and number recognition|[rec_chinese_common_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml)|94.8M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) |
@@ -58,45 +63,6 @@ Relationship of the above models is as follows.
#### Multilingual Recognition Model（Updating...）
-**Note：** The configuration file of the new multi language model is generated by code. You can use the `--help` parameter to check which multi language are supported by current PaddleOCR.
-
-```bash
-# The code needs to run in the specified directory
-cd {your/path/}PaddleOCR/configs/rec/multi_language/
-python3 generate_multi_language_configs.py --help
-```
-
-Take the Italian configuration file as an example：
-##### 1.Generate Italian configuration file to test the model provided
-you can generate the default configuration file through the following command, and use the default language dictionary provided by paddleocr for prediction.
-```bash
-# The code needs to run in the specified directory
-cd {your/path/}PaddleOCR/configs/rec/multi_language/
-# Set the required language configuration file through -l or --language parameter
-# This command will write the default parameter to the configuration file.
-python3 generate_multi_language_configs.py -l it
-```
-##### 2. Generate Italian configuration file to train your own data
-If you want to train your own model, you can prepare the training set file, verification set file, dictionary file and training data path. Here we assume that the Italian training set, verification set, dictionary and training data path are:
-- Training set:{your/path/}PaddleOCR/train_data/train_list.txt
-- Validation set: {your/path/}PaddleOCR/train_data/val_list.txt
-- Use the default dictionary provided by paddleocr:{your/path/}PaddleOCR/ppocr/utils/dict/it_dict.txt
-- Training data path:{your/path/}PaddleOCR/train_data
-```bash
-# The code needs to run in the specified directory
-cd {your/path/}PaddleOCR/configs/rec/multi_language/
-# The -l or --language parameter is required
-# --train modify train_list path
-# --val modify eval_list path
-# --data_dir modify data dir
-# -o modify default parameters
-# --dict Change the dictionary path. The example uses the default dictionary path, so that this parameter can be empty.
-python3 generate_multi_language_configs.py -l it \
---train {path/to/train_list} \
---val {path/to/val_list} \
---data_dir {path/to/data_dir} \
--o Global.use_gpu=False
-```
|model name| dict file | description|config|model size|download|
| --- | --- | --- |--- | --- | --- |
| french_mobile_v2.0_rec | ppocr/utils/dict/french_dict.txt | Lightweight model for French recognition|[rec_french_lite_train.yml](../../configs/rec/multi_language/rec_french_lite_train.yml)|2.65M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_train.tar) |
@@ -120,12 +86,14 @@ For more supported languages, please refer to : [Multi-language model](./multi_l
|model name|description|config|model size|download|
| --- | --- | --- | --- | --- |
-|ch_ppocr_mobile_slim_v2.0_cls|Slim quantized model|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)| 2.1M | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_slim_train.tar) |
-|ch_ppocr_mobile_v2.0_cls|Original model|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)|1.38M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |
+|ch_ppocr_mobile_slim_v2.0_cls|Slim quantized model for text angle classification|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)| 2.1M | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_slim_train.tar) |
+|ch_ppocr_mobile_v2.0_cls|Original model for text angle classification|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)|1.38M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |
### 4. Paddle-Lite Model
|Version|Introduction|Model size|Detection model|Text Direction model|Recognition model|Paddle-Lite branch|
|---|---|---|---|---|---|---|
-|V2.0|extra-lightweight chinese OCR optimized model|7.8M|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_opt.nb)|v2.9|
-|V2.0(slim)|extra-lightweight chinese OCR optimized model|3.3M|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_slim_opt.nb)|v2.9|
+|V2.1|ppocr_v2.1 extra-lightweight chinese OCR optimized model|11M|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_det_infer_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_rec_infer_opt.nb)|v2.9|
+|V2.1(slim)|extra-lightweight chinese OCR optimized model|4.9M|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_det_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_rec_slim_opt.nb)|v2.9|
+|V2.0|ppocr_v2.0 extra-lightweight chinese OCR optimized model|7.8M|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_opt.nb)|v2.9|
+|V2.0(slim)|ppovr_v2.0 extra-lightweight chinese OCR optimized model|3.3M|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_slim_opt.nb)|v2.9|
diff --git a/doc/doc_en/multi_languages_en.md b/doc/doc_en/multi_languages_en.md
index 43650c6ddfdd8c27ab44d0495111a767aeac9ca8..545be5524f2c52c9799d3b013f1aac8baf1a379f 100644
--- a/doc/doc_en/multi_languages_en.md
+++ b/doc/doc_en/multi_languages_en.md
@@ -198,13 +198,13 @@ If necessary, you can read related documents:
| Language | Abbreviation | | Language | Abbreviation |
| --- | --- | --- | --- | --- |
-|chinese and english|ch| |Arabic|ar|
-|english|en| |Hindi|hi|
-|french|fr| |Uyghur|ug|
-|german|german| |Persian|fa|
-|japan|japan| |Urdu|ur|
-|korean|korean| | Serbian(latin) |rs_latin|
-|chinese traditional |ch_tra| |Occitan |oc|
+|Chinese & English|ch| |Arabic|ar|
+|English|en| |Hindi|hi|
+|French|fr| |Uyghur|ug|
+|German|german| |Persian|fa|
+|Japan|japan| |Urdu|ur|
+|Korean|korean| | Serbian(latin) |rs_latin|
+|Chinese Traditional |chinese_cht| |Occitan |oc|
| Italian |it| |Marathi|mr|
|Spanish |es| |Nepali|ne|
| Portuguese|pt| |Serbian(cyrillic)|rs_cyrillic|
diff --git a/doc/doc_en/paddleOCR_overview_en.md b/doc/doc_en/paddleOCR_overview_en.md
new file mode 100644
index 0000000000000000000000000000000000000000..403cd99415e08de198270fb5bfe1a43f297c5156
--- /dev/null
+++ b/doc/doc_en/paddleOCR_overview_en.md
@@ -0,0 +1,39 @@
+# PaddleOCR Overview and Project Clone
+
+## 1. PaddleOCR Overview
+
+PaddleOCR contains rich text detection, text recognition and end-to-end algorithms. Combining actual testing and industrial experience, PaddleOCR chooses DB and CRNN as the basic detection and recognition models, and proposes a series of models, named PP-OCR, for industrial applications after a series of optimization strategies. The PP-OCR model is aimed at general scenarios and forms a model library according to different languages. Based on the capabilities of PP-OCR, PaddleOCR releases the PP-Structure tool library for document scene tasks, including two major tasks: layout analysis and table recognition. In order to get through the entire process of industrial landing, PaddleOCR provides large-scale data production tools and a variety of prediction deployment tools to help developers quickly turn ideas into reality.
+
+
+
+
+
+
+
+
## ch_ppocr_server_2.0
diff --git a/doc/doc_en/whl_en.md b/doc/doc_en/whl_en.md
index c8c8353accdf7f6ce179d3700547bfe9bd70c200..c2577e1e151e4675abab5139da099db9ad20fb4b 100644
--- a/doc/doc_en/whl_en.md
+++ b/doc/doc_en/whl_en.md
@@ -1,4 +1,4 @@
-# paddleocr package
+# Paddleocr Package
## 1 Get started quickly
### 1.1 install package
diff --git a/doc/ic15_location_download.png b/doc/ic15_location_download.png
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index 0000000000000000000000000000000000000000..c5c4e09d6730bb0b1ca2c0b5442079ceb41ecdfa
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diff --git a/doc/overview_en.png b/doc/overview_en.png
new file mode 100644
index 0000000000000000000000000000000000000000..b44da4e9874d6a2162a8bb05ff1b479875bd65f3
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diff --git a/doc/ppocrv2_framework.jpg b/doc/ppocrv2_framework.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e5f1a2ef47601c3a9eaef43a6046a15ea0319e2b
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diff --git a/paddleocr.py b/paddleocr.py
index 45c1a40dbbe3ad5cba88cadf0ced85717c7a23da..de712442450aaba1176d2cf754de8a429042f84d 100644
--- a/paddleocr.py
+++ b/paddleocr.py
@@ -33,104 +33,141 @@ from tools.infer.utility import draw_ocr, str2bool
from ppstructure.utility import init_args, draw_structure_result
from ppstructure.predict_system import OCRSystem, save_structure_res
-__all__ = ['PaddleOCR', 'PPStructure', 'draw_ocr', 'draw_structure_result', 'save_structure_res','download_with_progressbar']
-
-model_urls = {
- 'det': {
- 'ch':
- 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar',
- 'en':
- 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_ppocr_mobile_v2.0_det_infer.tar',
- 'structure': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar'
+__all__ = [
+ 'PaddleOCR', 'PPStructure', 'draw_ocr', 'draw_structure_result',
+ 'save_structure_res', 'download_with_progressbar'
+]
+
+SUPPORT_DET_MODEL = ['DB']
+VERSION = '2.2.1'
+SUPPORT_REC_MODEL = ['CRNN']
+BASE_DIR = os.path.expanduser("~/.paddleocr/")
+
+DEFAULT_MODEL_VERSION = '2.0'
+MODEL_URLS = {
+ '2.1': {
+ 'det': {
+ 'ch': {
+ 'url':
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_det_infer.tar',
+ },
+ },
+ 'rec': {
+ 'ch': {
+ 'url':
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_rec_infer.tar',
+ 'dict_path': './ppocr/utils/ppocr_keys_v1.txt'
+ }
+ }
},
- 'rec': {
- 'ch': {
- 'url':
- 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar',
- 'dict_path': './ppocr/utils/ppocr_keys_v1.txt'
+ '2.0': {
+ 'det': {
+ 'ch': {
+ 'url':
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar',
+ },
+ 'en': {
+ 'url':
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_ppocr_mobile_v2.0_det_infer.tar',
+ },
+ 'structure': {
+ 'url':
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar'
+ }
},
- 'en': {
- 'url':
+ 'rec': {
+ 'ch': {
+ 'url':
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar',
+ 'dict_path': './ppocr/utils/ppocr_keys_v1.txt'
+ },
+ 'en': {
+ 'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar',
- 'dict_path': './ppocr/utils/en_dict.txt'
- },
- 'french': {
- 'url':
+ 'dict_path': './ppocr/utils/en_dict.txt'
+ },
+ 'french': {
+ 'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar',
- 'dict_path': './ppocr/utils/dict/french_dict.txt'
- },
- 'german': {
- 'url':
+ 'dict_path': './ppocr/utils/dict/french_dict.txt'
+ },
+ 'german': {
+ 'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar',
- 'dict_path': './ppocr/utils/dict/german_dict.txt'
- },
- 'korean': {
- 'url':
+ 'dict_path': './ppocr/utils/dict/german_dict.txt'
+ },
+ 'korean': {
+ 'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar',
- 'dict_path': './ppocr/utils/dict/korean_dict.txt'
- },
- 'japan': {
- 'url':
+ 'dict_path': './ppocr/utils/dict/korean_dict.txt'
+ },
+ 'japan': {
+ 'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar',
- 'dict_path': './ppocr/utils/dict/japan_dict.txt'
- },
- 'chinese_cht': {
- 'url':
+ 'dict_path': './ppocr/utils/dict/japan_dict.txt'
+ },
+ 'chinese_cht': {
+ 'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar',
- 'dict_path': './ppocr/utils/dict/chinese_cht_dict.txt'
- },
- 'ta': {
- 'url':
+ 'dict_path': './ppocr/utils/dict/chinese_cht_dict.txt'
+ },
+ 'ta': {
+ 'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar',
- 'dict_path': './ppocr/utils/dict/ta_dict.txt'
- },
- 'te': {
- 'url':
+ 'dict_path': './ppocr/utils/dict/ta_dict.txt'
+ },
+ 'te': {
+ 'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar',
- 'dict_path': './ppocr/utils/dict/te_dict.txt'
- },
- 'ka': {
- 'url':
+ 'dict_path': './ppocr/utils/dict/te_dict.txt'
+ },
+ 'ka': {
+ 'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar',
- 'dict_path': './ppocr/utils/dict/ka_dict.txt'
- },
- 'latin': {
- 'url':
+ 'dict_path': './ppocr/utils/dict/ka_dict.txt'
+ },
+ 'latin': {
+ 'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar',
- 'dict_path': './ppocr/utils/dict/latin_dict.txt'
- },
- 'arabic': {
- 'url':
+ 'dict_path': './ppocr/utils/dict/latin_dict.txt'
+ },
+ 'arabic': {
+ 'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar',
- 'dict_path': './ppocr/utils/dict/arabic_dict.txt'
- },
- 'cyrillic': {
- 'url':
+ 'dict_path': './ppocr/utils/dict/arabic_dict.txt'
+ },
+ 'cyrillic': {
+ 'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar',
- 'dict_path': './ppocr/utils/dict/cyrillic_dict.txt'
- },
- 'devanagari': {
- 'url':
+ 'dict_path': './ppocr/utils/dict/cyrillic_dict.txt'
+ },
+ 'devanagari': {
+ 'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar',
- 'dict_path': './ppocr/utils/dict/devanagari_dict.txt'
+ 'dict_path': './ppocr/utils/dict/devanagari_dict.txt'
+ },
+ 'structure': {
+ 'url':
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar',
+ 'dict_path': 'ppocr/utils/dict/table_dict.txt'
+ }
+ },
+ 'cls': {
+ 'ch': {
+ 'url':
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar',
+ }
},
- 'structure': {
- 'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar',
- 'dict_path': 'ppocr/utils/dict/table_dict.txt'
+ 'table': {
+ 'en': {
+ 'url':
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar',
+ 'dict_path': 'ppocr/utils/dict/table_structure_dict.txt'
+ }
}
- },
- 'cls': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar',
- 'table': {
- 'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar',
- 'dict_path': 'ppocr/utils/dict/table_structure_dict.txt'
}
}
-SUPPORT_DET_MODEL = ['DB']
-VERSION = '2.2.0.1'
-SUPPORT_REC_MODEL = ['CRNN']
-BASE_DIR = os.path.expanduser("~/.paddleocr/")
-
def parse_args(mMain=True):
import argparse
@@ -140,6 +177,7 @@ def parse_args(mMain=True):
parser.add_argument("--det", type=str2bool, default=True)
parser.add_argument("--rec", type=str2bool, default=True)
parser.add_argument("--type", type=str, default='ocr')
+ parser.add_argument("--version", type=str, default='2.1')
for action in parser._actions:
if action.dest in ['rec_char_dict_path', 'table_char_dict_path']:
@@ -155,19 +193,19 @@ def parse_args(mMain=True):
def parse_lang(lang):
latin_lang = [
- 'af', 'az', 'bs', 'cs', 'cy', 'da', 'de', 'es', 'et', 'fr', 'ga',
- 'hr', 'hu', 'id', 'is', 'it', 'ku', 'la', 'lt', 'lv', 'mi', 'ms',
- 'mt', 'nl', 'no', 'oc', 'pi', 'pl', 'pt', 'ro', 'rs_latin', 'sk',
- 'sl', 'sq', 'sv', 'sw', 'tl', 'tr', 'uz', 'vi'
+ 'af', 'az', 'bs', 'cs', 'cy', 'da', 'de', 'es', 'et', 'fr', 'ga', 'hr',
+ 'hu', 'id', 'is', 'it', 'ku', 'la', 'lt', 'lv', 'mi', 'ms', 'mt', 'nl',
+ 'no', 'oc', 'pi', 'pl', 'pt', 'ro', 'rs_latin', 'sk', 'sl', 'sq', 'sv',
+ 'sw', 'tl', 'tr', 'uz', 'vi'
]
arabic_lang = ['ar', 'fa', 'ug', 'ur']
cyrillic_lang = [
- 'ru', 'rs_cyrillic', 'be', 'bg', 'uk', 'mn', 'abq', 'ady', 'kbd',
- 'ava', 'dar', 'inh', 'che', 'lbe', 'lez', 'tab'
+ 'ru', 'rs_cyrillic', 'be', 'bg', 'uk', 'mn', 'abq', 'ady', 'kbd', 'ava',
+ 'dar', 'inh', 'che', 'lbe', 'lez', 'tab'
]
devanagari_lang = [
- 'hi', 'mr', 'ne', 'bh', 'mai', 'ang', 'bho', 'mah', 'sck', 'new',
- 'gom', 'sa', 'bgc'
+ 'hi', 'mr', 'ne', 'bh', 'mai', 'ang', 'bho', 'mah', 'sck', 'new', 'gom',
+ 'sa', 'bgc'
]
if lang in latin_lang:
lang = "latin"
@@ -177,9 +215,9 @@ def parse_lang(lang):
lang = "cyrillic"
elif lang in devanagari_lang:
lang = "devanagari"
- assert lang in model_urls[
+ assert lang in MODEL_URLS[DEFAULT_MODEL_VERSION][
'rec'], 'param lang must in {}, but got {}'.format(
- model_urls['rec'].keys(), lang)
+ MODEL_URLS[DEFAULT_MODEL_VERSION]['rec'].keys(), lang)
if lang == "ch":
det_lang = "ch"
elif lang == 'structure':
@@ -189,6 +227,35 @@ def parse_lang(lang):
return lang, det_lang
+def get_model_config(version, model_type, lang):
+ if version not in MODEL_URLS:
+ logger.warning('version {} not in {}, use version {} instead'.format(
+ version, MODEL_URLS.keys(), DEFAULT_MODEL_VERSION))
+ version = DEFAULT_MODEL_VERSION
+ if model_type not in MODEL_URLS[version]:
+ if model_type in MODEL_URLS[DEFAULT_MODEL_VERSION]:
+ logger.warning(
+ 'version {} not support {} models, use version {} instead'.
+ format(version, model_type, DEFAULT_MODEL_VERSION))
+ version = DEFAULT_MODEL_VERSION
+ else:
+ logger.error('{} models is not support, we only support {}'.format(
+ model_type, MODEL_URLS[DEFAULT_MODEL_VERSION].keys()))
+ sys.exit(-1)
+ if lang not in MODEL_URLS[version][model_type]:
+ if lang in MODEL_URLS[DEFAULT_MODEL_VERSION][model_type]:
+ logger.warning('lang {} is not support in {}, use {} instead'.
+ format(lang, version, DEFAULT_MODEL_VERSION))
+ version = DEFAULT_MODEL_VERSION
+ else:
+ logger.error(
+ 'lang {} is not support, we only support {} for {} models'.
+ format(lang, MODEL_URLS[DEFAULT_MODEL_VERSION][model_type].keys(
+ ), model_type))
+ sys.exit(-1)
+ return MODEL_URLS[version][model_type][lang]
+
+
class PaddleOCR(predict_system.TextSystem):
def __init__(self, **kwargs):
"""
@@ -204,15 +271,21 @@ class PaddleOCR(predict_system.TextSystem):
lang, det_lang = parse_lang(params.lang)
# init model dir
- params.det_model_dir, det_url = confirm_model_dir_url(params.det_model_dir,
- os.path.join(BASE_DIR, VERSION, 'ocr', 'det', det_lang),
- model_urls['det'][det_lang])
- params.rec_model_dir, rec_url = confirm_model_dir_url(params.rec_model_dir,
- os.path.join(BASE_DIR, VERSION, 'ocr', 'rec', lang),
- model_urls['rec'][lang]['url'])
- params.cls_model_dir, cls_url = confirm_model_dir_url(params.cls_model_dir,
- os.path.join(BASE_DIR, VERSION, 'ocr', 'cls'),
- model_urls['cls'])
+ det_model_config = get_model_config(params.version, 'det', det_lang)
+ params.det_model_dir, det_url = confirm_model_dir_url(
+ params.det_model_dir,
+ os.path.join(BASE_DIR, VERSION, 'ocr', 'det', det_lang),
+ det_model_config['url'])
+ rec_model_config = get_model_config(params.version, 'rec', lang)
+ params.rec_model_dir, rec_url = confirm_model_dir_url(
+ params.rec_model_dir,
+ os.path.join(BASE_DIR, VERSION, 'ocr', 'rec', lang),
+ rec_model_config['url'])
+ cls_model_config = get_model_config(params.version, 'cls', 'ch')
+ params.cls_model_dir, cls_url = confirm_model_dir_url(
+ params.cls_model_dir,
+ os.path.join(BASE_DIR, VERSION, 'ocr', 'cls'),
+ cls_model_config['url'])
# download model
maybe_download(params.det_model_dir, det_url)
maybe_download(params.rec_model_dir, rec_url)
@@ -226,7 +299,8 @@ class PaddleOCR(predict_system.TextSystem):
sys.exit(0)
if params.rec_char_dict_path is None:
- params.rec_char_dict_path = str(Path(__file__).parent / model_urls['rec'][lang]['dict_path'])
+ params.rec_char_dict_path = str(
+ Path(__file__).parent / rec_model_config['dict_path'])
print(params)
# init det_model and rec_model
@@ -293,24 +367,32 @@ class PPStructure(OCRSystem):
lang, det_lang = parse_lang(params.lang)
# init model dir
- params.det_model_dir, det_url = confirm_model_dir_url(params.det_model_dir,
- os.path.join(BASE_DIR, VERSION, 'ocr', 'det', det_lang),
- model_urls['det'][det_lang])
- params.rec_model_dir, rec_url = confirm_model_dir_url(params.rec_model_dir,
- os.path.join(BASE_DIR, VERSION, 'ocr', 'rec', lang),
- model_urls['rec'][lang]['url'])
- params.table_model_dir, table_url = confirm_model_dir_url(params.table_model_dir,
- os.path.join(BASE_DIR, VERSION, 'ocr', 'table'),
- model_urls['table']['url'])
+ det_model_config = get_model_config(params.version, 'det', det_lang)
+ params.det_model_dir, det_url = confirm_model_dir_url(
+ params.det_model_dir,
+ os.path.join(BASE_DIR, VERSION, 'ocr', 'det', det_lang),
+ det_model_config['url'])
+ rec_model_config = get_model_config(params.version, 'rec', lang)
+ params.rec_model_dir, rec_url = confirm_model_dir_url(
+ params.rec_model_dir,
+ os.path.join(BASE_DIR, VERSION, 'ocr', 'rec', lang),
+ rec_model_config['url'])
+ table_model_config = get_model_config(params.version, 'table', 'en')
+ params.table_model_dir, table_url = confirm_model_dir_url(
+ params.table_model_dir,
+ os.path.join(BASE_DIR, VERSION, 'ocr', 'table'),
+ table_model_config['url'])
# download model
maybe_download(params.det_model_dir, det_url)
maybe_download(params.rec_model_dir, rec_url)
maybe_download(params.table_model_dir, table_url)
if params.rec_char_dict_path is None:
- params.rec_char_dict_path = str(Path(__file__).parent / model_urls['rec'][lang]['dict_path'])
+ params.rec_char_dict_path = str(
+ Path(__file__).parent / rec_model_config['dict_path'])
if params.table_char_dict_path is None:
- params.table_char_dict_path = str(Path(__file__).parent / model_urls['table']['dict_path'])
+ params.table_char_dict_path = str(
+ Path(__file__).parent / table_model_config['dict_path'])
print(params)
super().__init__(params)
@@ -374,4 +456,3 @@ def main():
for item in result:
item.pop('img')
logger.info(item)
-
diff --git a/ppstructure/README.md b/ppstructure/README.md
index a00d12d8edbf5bd20a5c7efd41cf69809861ea31..849c5c5667ff0532dfee35479715880192df0dc5 100644
--- a/ppstructure/README.md
+++ b/ppstructure/README.md
@@ -124,8 +124,6 @@ Most of the parameters are consistent with the paddleocr whl package, see [doc o
After running, each image will have a directory with the same name under the directory specified in the output field. Each table in the picture will be stored as an excel and figure area will be cropped and saved, the excel and image file name will be the coordinates of the table in the image.
## 4. PP-Structure Pipeline
-
-the process is as follows
In PP-Structure, the image will be analyzed by layoutparser first. In the layout analysis, the area in the image will be classified, including **text, title, image, list and table** 5 categories. For the first 4 types of areas, directly use the PP-OCR to complete the text detection and recognition. The table area will be converted to an excel file of the same table style via Table OCR.
diff --git a/tests/compare_results.py b/tests/compare_results.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c3fe4ea951aef122728a7aed7fc4ecaf8e7607e
--- /dev/null
+++ b/tests/compare_results.py
@@ -0,0 +1,133 @@
+import numpy as np
+import os
+import subprocess
+import json
+import argparse
+import glob
+
+
+def init_args():
+ parser = argparse.ArgumentParser()
+ # params for testing assert allclose
+ parser.add_argument("--atol", type=float, default=1e-3)
+ parser.add_argument("--rtol", type=float, default=1e-3)
+ parser.add_argument("--gt_file", type=str, default="")
+ parser.add_argument("--log_file", type=str, default="")
+ parser.add_argument("--precision", type=str, default="fp32")
+ return parser
+
+
+def parse_args():
+ parser = init_args()
+ return parser.parse_args()
+
+
+def run_shell_command(cmd):
+ p = subprocess.Popen(
+ cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
+ out, err = p.communicate()
+
+ if p.returncode == 0:
+ return out.decode('utf-8')
+ else:
+ return None
+
+
+def parser_results_from_log_by_name(log_path, names_list):
+ if not os.path.exists(log_path):
+ raise ValueError("The log file {} does not exists!".format(log_path))
+
+ if names_list is None or len(names_list) < 1:
+ return []
+
+ parser_results = {}
+ for name in names_list:
+ cmd = "grep {} {}".format(name, log_path)
+ outs = run_shell_command(cmd)
+ outs = outs.split("\n")[0]
+ result = outs.split("{}".format(name))[-1]
+ result = json.loads(result)
+ parser_results[name] = result
+ return parser_results
+
+
+def load_gt_from_file(gt_file):
+ if not os.path.exists(gt_file):
+ raise ValueError("The log file {} does not exists!".format(gt_file))
+ with open(gt_file, 'r') as f:
+ data = f.readlines()
+ f.close()
+ parser_gt = {}
+ for line in data:
+ image_name, result = line.strip("\n").split("\t")
+ result = json.loads(result)
+ parser_gt[image_name] = result
+ return parser_gt
+
+
+def load_gt_from_txts(gt_file):
+ gt_list = glob.glob(gt_file)
+ gt_collection = {}
+ for gt_f in gt_list:
+ gt_dict = load_gt_from_file(gt_f)
+ basename = os.path.basename(gt_f)
+ if "fp32" in basename:
+ gt_collection["fp32"] = [gt_dict, gt_f]
+ elif "fp16" in basename:
+ gt_collection["fp16"] = [gt_dict, gt_f]
+ elif "int8" in basename:
+ gt_collection["int8"] = [gt_dict, gt_f]
+ else:
+ continue
+ return gt_collection
+
+
+def collect_predict_from_logs(log_path, key_list):
+ log_list = glob.glob(log_path)
+ pred_collection = {}
+ for log_f in log_list:
+ pred_dict = parser_results_from_log_by_name(log_f, key_list)
+ key = os.path.basename(log_f)
+ pred_collection[key] = pred_dict
+
+ return pred_collection
+
+
+def testing_assert_allclose(dict_x, dict_y, atol=1e-7, rtol=1e-7):
+ for k in dict_x:
+ np.testing.assert_allclose(
+ np.array(dict_x[k]), np.array(dict_y[k]), atol=atol, rtol=rtol)
+
+
+if __name__ == "__main__":
+ # Usage:
+ # python3.7 tests/compare_results.py --gt_file=./tests/results/*.txt --log_file=./tests/output/infer_*.log
+
+ args = parse_args()
+
+ gt_collection = load_gt_from_txts(args.gt_file)
+ key_list = gt_collection["fp32"][0].keys()
+
+ pred_collection = collect_predict_from_logs(args.log_file, key_list)
+ for filename in pred_collection.keys():
+ if "fp32" in filename:
+ gt_dict, gt_filename = gt_collection["fp32"]
+ elif "fp16" in filename:
+ gt_dict, gt_filename = gt_collection["fp16"]
+ elif "int8" in filename:
+ gt_dict, gt_filename = gt_collection["int8"]
+ else:
+ continue
+ pred_dict = pred_collection[filename]
+
+ try:
+ testing_assert_allclose(
+ gt_dict, pred_dict, atol=args.atol, rtol=args.rtol)
+ print(
+ "Assert allclose passed! The results of {} and {} are consistent!".
+ format(filename, gt_filename))
+ except Exception as E:
+ print(E)
+ raise ValueError(
+ "The results of {} and the results of {} are inconsistent!".
+ format(filename, gt_filename))
diff --git a/tests/results/det_results_gpu_fp32.txt b/tests/results/det_results_gpu_fp32.txt
new file mode 100644
index 0000000000000000000000000000000000000000..28af26d0b2178802baf45c58586d38f9eeffe820
--- /dev/null
+++ b/tests/results/det_results_gpu_fp32.txt
@@ -0,0 +1,49 @@
+00008790.jpg [[[209, 406], [280, 406], [280, 419], [209, 419]], [[60, 398], [105, 398], [105, 411], [60, 411]], [[198, 389], [291, 389], [291, 402], [198, 402]], [[162, 391], [173, 391], [173, 401], [162, 401]], [[35, 380], [133, 380], [133, 393], [35, 393]], [[199, 371], [292, 371], [292, 384], [199, 384]], [[218, 310], [272, 310], [272, 324], [218, 324]], [[162, 305], [172, 305], [172, 314], [162, 314]], [[371, 302], [436, 302], [436, 316], [371, 316]], [[31, 302], [134, 301], [134, 315], [31, 316]], [[223, 292], [269, 292], [269, 306], [223, 306]], [[60, 225], [104, 225], [104, 236], [60, 236]], [[218, 223], [272, 223], [272, 237], [218, 237]], [[162, 219], [173, 219], [173, 227], [162, 227]], [[33, 207], [131, 207], [131, 220], [33, 220]], [[223, 206], [269, 206], [269, 220], [223, 220]], [[74, 146], [383, 146], [383, 159], [74, 159]], [[54, 120], [117, 120], [117, 134], [54, 134]], [[74, 51], [296, 51], [296, 65], [74, 65]], [[56, 18], [116, 18], [116, 32], [56, 32]]]
+00018946.jpg [[[441, 328], [474, 328], [474, 339], [441, 339]], [[86, 284], [141, 286], [140, 307], [85, 305]], [[302, 279], [377, 279], [377, 297], [302, 297]], [[197, 265], [281, 274], [279, 293], [195, 284]], [[198, 197], [452, 219], [450, 242], [196, 220]], [[343, 182], [376, 182], [376, 192], [343, 192]], [[199, 164], [340, 171], [339, 192], [198, 185]], [[177, 101], [415, 118], [413, 145], [175, 128]]]
+00034387.jpg [[[265, 460], [740, 460], [740, 484], [265, 484]], [[348, 417], [420, 417], [420, 443], [348, 443]], [[545, 418], [568, 418], [568, 442], [545, 442]], [[685, 417], [710, 417], [710, 443], [685, 443]], [[175, 415], [226, 415], [226, 443], [175, 443]], [[874, 414], [908, 414], [908, 446], [874, 446]], [[56, 417], [74, 417], [74, 442], [56, 442]], [[856, 373], [925, 373], [925, 400], [856, 400]], [[348, 372], [418, 372], [418, 397], [348, 397]], [[674, 372], [723, 372], [723, 401], [674, 401]], [[539, 373], [570, 373], [570, 400], [539, 400]], [[151, 365], [228, 369], [226, 402], [149, 398]], [[56, 372], [74, 372], [74, 397], [56, 397]], [[857, 329], [925, 329], [925, 355], [857, 355]], [[351, 330], [419, 330], [419, 356], [351, 356]], [[674, 328], [723, 328], [723, 356], [674, 356]], [[541, 329], [570, 329], [570, 357], [541, 357]], [[171, 327], [227, 324], [229, 355], [173, 358]], [[57, 330], [74, 330], [74, 356], [57, 356]], [[298, 327], [316, 327], [316, 334], [298, 334]], [[855, 286], [925, 286], [925, 312], [855, 312]], [[674, 286], [723, 286], [723, 313], [674, 313]], [[346, 286], [426, 283], [427, 313], [347, 316]], [[540, 285], [569, 285], [569, 312], [540, 312]], [[172, 282], [226, 282], [226, 313], [172, 313]], [[56, 287], [73, 287], [73, 312], [56, 312]], [[857, 242], [925, 242], [925, 268], [857, 268]], [[348, 242], [460, 242], [460, 268], [348, 268]], [[156, 242], [227, 242], [227, 269], [156, 269]], [[674, 241], [724, 241], [724, 269], [674, 269]], [[531, 241], [572, 241], [572, 270], [531, 270]], [[56, 242], [74, 242], [74, 268], [56, 268]], [[855, 197], [925, 200], [924, 226], [854, 224]], [[674, 198], [726, 198], [726, 226], [674, 226]], [[344, 200], [434, 195], [436, 223], [346, 228]], [[176, 197], [227, 197], [227, 225], [176, 225]], [[56, 200], [73, 200], [73, 226], [56, 226]], [[527, 194], [576, 194], [576, 226], [527, 226]], [[349, 155], [419, 155], [419, 181], [349, 181]], [[854, 154], [925, 154], [925, 180], [854, 180]], [[176, 154], [226, 154], [226, 183], [176, 183]], [[670, 153], [723, 153], [723, 181], [670, 181]], [[538, 154], [571, 154], [571, 182], [538, 182]], [[56, 156], [74, 156], [74, 182], [56, 182]], [[349, 111], [419, 111], [419, 137], [349, 137]], [[174, 111], [227, 111], [227, 139], [174, 139]], [[546, 113], [564, 113], [564, 137], [546, 137]], [[52, 112], [75, 112], [75, 139], [52, 139]], [[639, 108], [727, 105], [728, 138], [640, 141]], [[817, 103], [927, 110], [925, 139], [815, 132]], [[814, 68], [951, 68], [951, 92], [814, 92]], [[307, 66], [446, 68], [446, 93], [306, 90]], [[673, 67], [723, 67], [723, 93], [673, 93]], [[175, 65], [228, 68], [226, 95], [174, 92]], [[39, 65], [90, 68], [88, 97], [37, 94]], [[528, 65], [580, 65], [580, 94], [528, 94]], [[334, 20], [670, 20], [670, 43], [334, 43]]]
+00037951.jpg [[[434, 976], [551, 978], [550, 993], [434, 991]], [[433, 932], [553, 932], [553, 969], [433, 969]], [[30, 522], [98, 522], [98, 545], [30, 545]], [[31, 443], [145, 443], [145, 464], [31, 464]], [[234, 335], [326, 332], [327, 354], [235, 356]], [[124, 252], [436, 252], [436, 284], [124, 284]], [[182, 206], [378, 206], [378, 227], [182, 227]], [[258, 106], [320, 123], [304, 181], [242, 163]], [[28, 65], [33, 65], [33, 71], [28, 71]], [[37, 58], [147, 58], [147, 80], [37, 80]]]
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diff --git a/tests/results/det_results_gpu_trt_fp16.txt b/tests/results/det_results_gpu_trt_fp16.txt
new file mode 100644
index 0000000000000000000000000000000000000000..191bdaf7807dad9129eb965f4ac81dadc9572af6
--- /dev/null
+++ b/tests/results/det_results_gpu_trt_fp16.txt
@@ -0,0 +1,49 @@
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253], [142, 249]], [[494, 226], [531, 226], [531, 239], [494, 239]], [[335, 226], [354, 226], [354, 237], [335, 237]], [[288, 226], [314, 226], [314, 237], [288, 237]], [[63, 226], [113, 226], [113, 236], [63, 236]], [[7, 227], [17, 227], [17, 234], [7, 234]], [[221, 225], [248, 225], [248, 235], [221, 235]], [[143, 225], [165, 222], [167, 234], [145, 237]], [[24, 224], [48, 224], [48, 238], [24, 238]], [[495, 213], [524, 213], [524, 224], [495, 224]], [[420, 212], [437, 212], [437, 225], [420, 225]], [[336, 212], [398, 212], [398, 223], [336, 223]], [[292, 212], [320, 212], [320, 223], [292, 223]], [[222, 212], [249, 212], [249, 223], [222, 223]], [[145, 212], [166, 212], [166, 223], [145, 223]], [[61, 211], [113, 209], [114, 222], [62, 224]], [[26, 211], [48, 211], [48, 223], [26, 223]], [[337, 199], [383, 199], [383, 209], [337, 209]], [[65, 200], [87, 200], [87, 207], [65, 207]], [[493, 197], [541, 197], [541, 211], [493, 211]], [[445, 202], [455, 196], [462, 206], [452, 212]], [[178, 198], [205, 198], [205, 208], [178, 208]], [[146, 199], [157, 199], [157, 208], [146, 208]], [[32, 194], [43, 204], [33, 214], [22, 203]], [[422, 193], [440, 201], [432, 215], [415, 207]], [[65, 186], [132, 186], [132, 196], [65, 196]], [[337, 185], [399, 185], [399, 196], [337, 196]], [[445, 190], [456, 182], [465, 191], [454, 200]], [[292, 188], [308, 182], [313, 193], [297, 200]], [[220, 183], [255, 183], [255, 197], [220, 197]], [[142, 184], [158, 184], [158, 197], [142, 197]], [[493, 182], [518, 182], [518, 197], [493, 197]], [[425, 180], [437, 191], [427, 202], [414, 190]], [[32, 179], [42, 189], [32, 199], [22, 189]], [[182, 179], [195, 185], [188, 198], [175, 192]], [[335, 172], [400, 169], [400, 183], [336, 185]], [[492, 170], [519, 170], [519, 185], [492, 185]], [[412, 177], [428, 164], [440, 178], [425, 190]], [[293, 171], [315, 171], [315, 185], [293, 185]], [[220, 170], [251, 170], [251, 184], [220, 184]], [[178, 172], [188, 172], [188, 183], [178, 183]], [[64, 172], [125, 170], [125, 181], [64, 182]], [[454, 168], [464, 176], [454, 185], [445, 176]], [[142, 172], [159, 168], [163, 180], [145, 185]], [[30, 165], [43, 174], [34, 186], [20, 177]], [[493, 160], [523, 160], [523, 170], [493, 170]], [[402, 161], [435, 161], [435, 168], [402, 168]], [[335, 159], [401, 159], [401, 169], [335, 169]], [[296, 159], [325, 159], [325, 170], [296, 170]], [[221, 158], [251, 158], [251, 169], [221, 169]], [[174, 161], [183, 156], [190, 167], [181, 172]], [[145, 158], [162, 158], [162, 170], [145, 170]], [[61, 158], [125, 157], [125, 168], [62, 169]], [[20, 161], [33, 154], [40, 167], [28, 174]], [[492, 143], [542, 143], [542, 157], [492, 157]], [[450, 144], [479, 144], [479, 157], [450, 157]], [[335, 143], [439, 143], [439, 156], [335, 156]], [[294, 143], [327, 143], [327, 157], [294, 157]], [[220, 143], [253, 143], [253, 157], [220, 157]], [[178, 145], [187, 145], [187, 156], [178, 156]], [[63, 144], [104, 144], [104, 155], [63, 155]], [[144, 140], [164, 145], [160, 159], [141, 154]], [[31, 137], [44, 149], [31, 162], [17, 149]], [[286, 135], [291, 135], [291, 140], [286, 140]], [[177, 133], [193, 133], [193, 144], [177, 144]], [[336, 132], [388, 132], [388, 141], [336, 141]], [[492, 131], [525, 131], [525, 141], [492, 141]], [[450, 131], [477, 131], [477, 141], [450, 141]], [[292, 131], [321, 131], [321, 141], [292, 141]], [[218, 132], [255, 130], [256, 141], [219, 144]], [[63, 131], [95, 131], [95, 141], [63, 141]], [[417, 130], [437, 130], [437, 141], [417, 141]], [[145, 130], [159, 130], [159, 143], [145, 143]], [[30, 124], [43, 133], [32, 147], [19, 138]], [[493, 118], [535, 118], [535, 129], [493, 129]], [[336, 118], [388, 118], [388, 129], [336, 129]], [[218, 118], [255, 118], [255, 128], [218, 128]], [[451, 117], [478, 117], [478, 129], [451, 129]], [[418, 117], [438, 117], [438, 130], [418, 130]], [[177, 116], [209, 116], [209, 130], [177, 130]], [[145, 117], [162, 117], [162, 130], [145, 130]], [[62, 116], [88, 116], [88, 131], [62, 131]], [[19, 121], [33, 111], [43, 124], [29, 134]], [[491, 107], [523, 107], [523, 113], [491, 113]], [[449, 107], [477, 107], [477, 113], [449, 113]], [[420, 107], [436, 107], [436, 113], [420, 113]], [[295, 107], [319, 107], [319, 114], [295, 114]], [[220, 107], [242, 107], [242, 113], [220, 113]], [[176, 107], [203, 107], [203, 113], [176, 113]], [[145, 107], [161, 107], [161, 114], [145, 114]], [[334, 105], [372, 105], [372, 114], [334, 114]], [[63, 106], [86, 106], [86, 113], [63, 113]], [[483, 89], [522, 89], [522, 99], [483, 99]], [[331, 88], [380, 88], [380, 99], [331, 99]], [[276, 88], [325, 88], [325, 99], [276, 99]], [[214, 88], [246, 88], [246, 99], [214, 99]], [[411, 86], [474, 86], [474, 100], [411, 100]], [[6, 86], [102, 86], [102, 100], [6, 100]], [[415, 66], [461, 66], [461, 77], [415, 77]], [[288, 66], [333, 66], [333, 77], [288, 77]], [[157, 64], [206, 64], [206, 78], [157, 78]], [[416, 48], [523, 49], [523, 63], [415, 62]], [[288, 49], [375, 49], [375, 63], [288, 63]], [[159, 49], [269, 49], [269, 62], [159, 62]], [[24, 53], [36, 46], [45, 59], [33, 67]], [[416, 36], [481, 36], [481, 46], [416, 46]], [[25, 38], [39, 32], [46, 46], [33, 52]], [[157, 34], [205, 34], [205, 47], [157, 47]], [[412, 4], [527, 4], [527, 17], [412, 17]], [[146, 4], [345, 2], [345, 15], [146, 17]]]
+test_add_20.jpg [[[31, 346], [605, 346], [605, 370], [31, 370]], [[217, 294], [510, 294], [510, 322], [217, 322]], [[473, 271], [525, 271], [525, 286], [473, 286]], [[220, 267], [287, 267], [287, 286], [220, 286]], [[219, 239], [484, 239], [484, 263], [219, 263]], [[221, 217], [303, 217], [303, 234], [221, 234]], [[402, 192], [417, 192], [417, 205], [402, 205]], [[222, 187], [341, 187], [341, 207], [222, 207]], [[221, 162], [287, 162], [287, 180], [221, 180]], [[375, 122], [475, 124], [475, 146], [375, 143]], [[222, 124], [356, 122], [356, 143], [222, 146]], [[218, 81], [352, 84], [352, 116], [218, 113]], [[440, 35], [605, 35], [605, 60], [440, 60]], [[72, 16], [398, 16], [398, 44], [72, 44]]]
+test_add_3.jpg [[[169, 327], [337, 326], [337, 341], [169, 342]], [[170, 288], [307, 290], [307, 312], [170, 310]], [[171, 221], [323, 221], [323, 234], [171, 234]], [[340, 221], [449, 217], [449, 231], [341, 234]], [[169, 201], [372, 201], [372, 214], [169, 214]], [[170, 183], [418, 183], [418, 196], [170, 196]], [[170, 149], [416, 149], [416, 163], [170, 163]], [[171, 119], [418, 119], [418, 140], [171, 140]], [[326, 64], [478, 64], [478, 91], [326, 91]], [[173, 64], [306, 60], [306, 89], [174, 93]]]
+test_add_4.png []
+test_add_5.png [[[48, 164], [108, 164], [108, 174], [48, 174]], [[52, 121], [169, 121], [169, 134], [52, 134]], [[50, 102], [165, 102], [165, 118], [50, 118]], [[52, 83], [164, 83], [164, 100], [52, 100]], [[51, 68], [166, 68], [166, 84], [51, 84]], [[51, 50], [145, 47], [145, 64], [52, 67]]]
+test_add_6.jpg [[[123, 223], [219, 227], [218, 251], [122, 247]], [[172, 172], [186, 186], [172, 200], [158, 186]]]
+test_add_7.jpg [[[48, 938], [174, 936], [174, 962], [48, 964]], [[227, 873], [629, 876], [628, 953], [226, 949]], [[56, 745], [638, 745], [638, 790], [56, 790]], [[150, 674], [545, 678], [544, 721], [150, 718]], [[73, 504], [633, 504], [633, 601], [73, 601]], [[59, 270], [655, 279], [652, 441], [56, 432]], [[513, 193], [553, 193], [553, 223], [513, 223]], [[61, 175], [532, 175], [532, 239], [61, 239]], [[533, 178], [642, 178], [642, 236], [533, 236]]]
+test_add_8.jpg [[[251, 586], [454, 580], [454, 606], [252, 613]], [[107, 533], [457, 527], [457, 560], [108, 566]], [[336, 494], [384, 494], [384, 507], [336, 507]], [[27, 307], [355, 297], [356, 320], [28, 330]], [[22, 259], [445, 251], [445, 274], [23, 282]], [[78, 209], [445, 205], [445, 225], [78, 229]], [[160, 23], [319, 30], [317, 79], [158, 72]]]
+test_add_9.png [[[266, 687], [486, 687], [486, 696], [266, 696]], [[196, 668], [554, 668], [554, 681], [196, 681]], [[154, 596], [597, 596], [597, 606], [154, 606]], [[215, 578], [541, 578], [541, 588], [215, 588]], [[134, 560], [615, 560], [615, 570], [134, 570]], [[85, 543], [665, 543], [665, 553], [85, 553]], [[96, 522], [653, 522], [653, 535], [96, 535]], [[362, 449], [389, 449], [389, 460], [362, 460]], [[238, 376], [513, 376], [513, 389], [238, 389]], [[177, 356], [574, 356], [574, 368], [177, 368]], [[344, 281], [408, 283], [407, 297], [343, 294]], [[257, 205], [493, 205], [493, 219], [257, 219]]]
diff --git a/tools/eval.py b/tools/eval.py
index fb8c7925adac33b0693792a6736c603511e3e3ae..39a26ffefff46a9a3fe1465e874d501a334921c7 100755
--- a/tools/eval.py
+++ b/tools/eval.py
@@ -27,7 +27,7 @@ from ppocr.data import build_dataloader
from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process
from ppocr.metrics import build_metric
-from ppocr.utils.save_load import init_model, load_pretrained_params
+from ppocr.utils.save_load import init_model, load_dygraph_params
from ppocr.utils.utility import print_dict
import tools.program as program
@@ -61,7 +61,7 @@ def main():
else:
model_type = None
- best_model_dict = init_model(config, model)
+ best_model_dict = load_dygraph_params(config, model, logger, None)
if len(best_model_dict):
logger.info('metric in ckpt ***************')
for k, v in best_model_dict.items():
diff --git a/tools/export_model.py b/tools/export_model.py
index 785aca10e46200bda49bdff2b89ba00cafbe7a20..cae87aca129134d64711e364bf10428d69500a06 100755
--- a/tools/export_model.py
+++ b/tools/export_model.py
@@ -93,6 +93,9 @@ def main():
for key in config["Architecture"]["Models"]:
config["Architecture"]["Models"][key]["Head"][
"out_channels"] = char_num
+ # just one final tensor needs to to exported for inference
+ config["Architecture"]["Models"][key][
+ "return_all_feats"] = False
else: # base rec model
config["Architecture"]["Head"]["out_channels"] = char_num
model = build_model(config["Architecture"])
diff --git a/tools/infer/predict_det.py b/tools/infer/predict_det.py
index 5c75e0c480eac6796d6d4b7075d1b38d254380fd..6347ca6dc719f0d489736dbf285eedd775d3790e 100755
--- a/tools/infer/predict_det.py
+++ b/tools/infer/predict_det.py
@@ -30,7 +30,7 @@ from ppocr.utils.logging import get_logger
from ppocr.utils.utility import get_image_file_list, check_and_read_gif
from ppocr.data import create_operators, transform
from ppocr.postprocess import build_post_process
-
+import json
logger = get_logger()
@@ -243,6 +243,7 @@ if __name__ == "__main__":
if not os.path.exists(draw_img_save):
os.makedirs(draw_img_save)
+ save_results = []
for image_file in image_file_list:
img, flag = check_and_read_gif(image_file)
if not flag:
@@ -256,8 +257,11 @@ if __name__ == "__main__":
if count > 0:
total_time += elapse
count += 1
-
- logger.info("Predict time of {}: {}".format(image_file, elapse))
+ save_pred = os.path.basename(image_file) + "\t" + str(
+ json.dumps(np.array(dt_boxes).astype(np.int32).tolist())) + "\n"
+ save_results.append(save_pred)
+ logger.info(save_pred)
+ logger.info("The predict time of {}: {}".format(image_file, elapse))
src_im = utility.draw_text_det_res(dt_boxes, image_file)
img_name_pure = os.path.split(image_file)[-1]
img_path = os.path.join(draw_img_save,
@@ -265,5 +269,8 @@ if __name__ == "__main__":
cv2.imwrite(img_path, src_im)
logger.info("The visualized image saved in {}".format(img_path))
+ with open(os.path.join(draw_img_save, "det_results.txt"), 'w') as f:
+ f.writelines(save_results)
+ f.close()
if args.benchmark:
text_detector.autolog.report()
diff --git a/tools/infer/utility.py b/tools/infer/utility.py
index 707328f28468db86c5061795d04713dc3b21a5cb..7f60773c3e76aa4bf66caeb29dc2968be49cc51a 100755
--- a/tools/infer/utility.py
+++ b/tools/infer/utility.py
@@ -35,7 +35,7 @@ def init_args():
parser.add_argument("--use_gpu", type=str2bool, default=True)
parser.add_argument("--ir_optim", type=str2bool, default=True)
parser.add_argument("--use_tensorrt", type=str2bool, default=False)
- parser.add_argument("--min_subgraph_size", type=int, default=10)
+ parser.add_argument("--min_subgraph_size", type=int, default=15)
parser.add_argument("--precision", type=str, default="fp32")
parser.add_argument("--gpu_mem", type=int, default=500)
diff --git a/tools/infer_det.py b/tools/infer_det.py
index a964cd28c934504ce79ea4873d3345295c1266e5..ce16da8dc5fffb3f5fdc633aeb00a386a2d60d4f 100755
--- a/tools/infer_det.py
+++ b/tools/infer_det.py
@@ -34,23 +34,21 @@ import paddle
from ppocr.data import create_operators, transform
from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process
-from ppocr.utils.save_load import init_model
+from ppocr.utils.save_load import init_model, load_dygraph_params
from ppocr.utils.utility import get_image_file_list
import tools.program as program
-def draw_det_res(dt_boxes, config, img, img_name):
+def draw_det_res(dt_boxes, config, img, img_name, save_path):
if len(dt_boxes) > 0:
import cv2
src_im = img
for box in dt_boxes:
box = box.astype(np.int32).reshape((-1, 1, 2))
cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
- save_det_path = os.path.dirname(config['Global'][
- 'save_res_path']) + "/det_results/"
- if not os.path.exists(save_det_path):
- os.makedirs(save_det_path)
- save_path = os.path.join(save_det_path, os.path.basename(img_name))
+ if not os.path.exists(save_path):
+ os.makedirs(save_path)
+ save_path = os.path.join(save_path, os.path.basename(img_name))
cv2.imwrite(save_path, src_im)
logger.info("The detected Image saved in {}".format(save_path))
@@ -61,8 +59,7 @@ def main():
# build model
model = build_model(config['Architecture'])
- init_model(config, model)
-
+ _ = load_dygraph_params(config, model, logger, None)
# build post process
post_process_class = build_post_process(config['PostProcess'])
@@ -96,17 +93,41 @@ def main():
images = paddle.to_tensor(images)
preds = model(images)
post_result = post_process_class(preds, shape_list)
- boxes = post_result[0]['points']
- # write result
+
+ src_img = cv2.imread(file)
+
dt_boxes_json = []
- for box in boxes:
- tmp_json = {"transcription": ""}
- tmp_json['points'] = box.tolist()
- dt_boxes_json.append(tmp_json)
+ # parser boxes if post_result is dict
+ if isinstance(post_result, dict):
+ det_box_json = {}
+ for k in post_result.keys():
+ boxes = post_result[k][0]['points']
+ dt_boxes_list = []
+ for box in boxes:
+ tmp_json = {"transcription": ""}
+ tmp_json['points'] = box.tolist()
+ dt_boxes_list.append(tmp_json)
+ det_box_json[k] = dt_boxes_list
+ save_det_path = os.path.dirname(config['Global'][
+ 'save_res_path']) + "/det_results_{}/".format(k)
+ draw_det_res(boxes, config, src_img, file, save_det_path)
+ else:
+ boxes = post_result[0]['points']
+ dt_boxes_json = []
+ # write result
+ for box in boxes:
+ tmp_json = {"transcription": ""}
+ tmp_json['points'] = box.tolist()
+ dt_boxes_json.append(tmp_json)
+ save_det_path = os.path.dirname(config['Global'][
+ 'save_res_path']) + "/det_results/"
+ draw_det_res(boxes, config, src_img, file, save_det_path)
otstr = file + "\t" + json.dumps(dt_boxes_json) + "\n"
fout.write(otstr.encode())
- src_img = cv2.imread(file)
- draw_det_res(boxes, config, src_img, file)
+
+ save_det_path = os.path.dirname(config['Global'][
+ 'save_res_path']) + "/det_results/"
+ draw_det_res(boxes, config, src_img, file, save_det_path)
logger.info("success!")
diff --git a/tools/infer_rec.py b/tools/infer_rec.py
index f16cd7d327987db2630743e14c633ddf3743f8d3..29d4b530dfcfb8a3201e12b38c9b9f186f34b627 100755
--- a/tools/infer_rec.py
+++ b/tools/infer_rec.py
@@ -130,7 +130,7 @@ def main():
if len(post_result[key][0]) >= 2:
rec_info[key] = {
"label": post_result[key][0][0],
- "score": post_result[key][0][1],
+ "score": float(post_result[key][0][1]),
}
info = json.dumps(rec_info)
else: