@@ -120,8 +120,8 @@ For a new language request, please refer to [Guideline for new language_requests
- [PP-Structure: Information Extraction](./ppstructure/README.md)
- [Layout Parser](./ppstructure/layout/README.md)
- [Table Recognition](./ppstructure/table/README.md)
- - [DocVQA](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.4/ppstructure/vqa)
- - [Key Information Extraction](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.4/ppstructure/docs/kie.md)
+ - [DocVQA](./ppstructure/vqa/README.md)
+ - [Key Information Extraction](./ppstructure/docs/kie.md)
- Academic Circles
- [Two-stage Algorithm](./doc/doc_en/algorithm_overview_en.md)
- [PGNet Algorithm](./doc/doc_en/pgnet_en.md)
diff --git a/README_ch.md b/README_ch.md
index 77da6121dbe6d757558f08e1dc69aabbe05e114c..33d548c44b5ccd21e55882acbde8637551ebd668 100755
--- a/README_ch.md
+++ b/README_ch.md
@@ -99,8 +99,8 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
- [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)
- - [DocVQA](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.4/ppstructure/vqa)
- - [关键信息提取](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.4/ppstructure/docs/kie.md)
+ - [DocVQA](./ppstructure/vqa/README_ch.md)
+ - [关键信息提取](./ppstructure/docs/kie.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)
diff --git a/deploy/cpp_infer/readme_en.md b/deploy/cpp_infer/readme_en.md
index 0ed71840ccb904ea99180ef72efcda4433c50e0d..2aca6a9143c1d4616662b75e964e4705976655f1 100644
--- a/deploy/cpp_infer/readme_en.md
+++ b/deploy/cpp_infer/readme_en.md
@@ -1,9 +1,8 @@
# Server-side C++ Inference
-This chapter introduces the C++ deployment method of the PaddleOCR model, and the corresponding python predictive deployment method refers to [document](../../doc/doc_ch/inference.md).
-C++ is better than python in terms of performance calculation. Therefore, in most CPU and GPU deployment scenarios, C++ deployment is mostly used.
-This section will introduce how to configure the C++ environment and complete it in the Linux\Windows (CPU\GPU) environment
-PaddleOCR model deployment.
+This chapter introduces the C++ deployment steps of the PaddleOCR model. The corresponding Python predictive deployment method refers to [document](../../doc/doc_ch/inference.md).
+C++ is better than python in terms of performance. Therefore, in CPU and GPU deployment scenarios, C++ deployment is mostly used.
+This section will introduce how to configure the C++ environment and deploy PaddleOCR in Linux (CPU\GPU) environment. For Windows deployment please refer to [Windows](./docs/windows_vs2019_build.md) compilation guidelines.
## 1. Prepare the Environment
@@ -15,7 +14,7 @@ PaddleOCR model deployment.
### 1.1 Compile OpenCV
-* First of all, you need to download the source code compiled package in the Linux environment from the opencv official website. Taking opencv3.4.7 as an example, the download command is as follows.
+* First of all, you need to download the source code compiled package in the Linux environment from the OpenCV official website. Taking OpenCV 3.4.7 as an example, the download command is as follows.
```bash
cd deploy/cpp_infer
@@ -23,9 +22,9 @@ wget https://paddleocr.bj.bcebos.com/libs/opencv/opencv-3.4.7.tar.gz
tar -xf opencv-3.4.7.tar.gz
```
-Finally, you can see the folder of `opencv-3.4.7/` in the current directory.
+Finally, you will see the folder of `opencv-3.4.7/` in the current directory.
-* Compile opencv, the opencv source path (`root_path`) and installation path (`install_path`) should be set by yourself. Enter the opencv source code path and compile it in the following way.
+* Compile OpenCV, the OpenCV source path (`root_path`) and installation path (`install_path`) should be set by yourself. Enter the OpenCV source code path and compile it in the following way.
```shell
@@ -58,11 +57,11 @@ make -j
make install
```
-Among them, `root_path` is the downloaded opencv source code path, and `install_path` is the installation path of opencv. After `make install` is completed, the opencv header file and library file will be generated in this folder for later OCR source code compilation.
+In the above commands, `root_path` is the downloaded OpenCV source code path, and `install_path` is the installation path of OpenCV. After `make install` is completed, the OpenCV header file and library file will be generated in this folder for later OCR source code compilation.
-The final file structure under the opencv installation path is as follows.
+The final file structure under the OpenCV installation path is as follows.
```
opencv3/
@@ -79,20 +78,20 @@ opencv3/
#### 1.2.1 Direct download and installation
-[Paddle inference library official website](https://paddle-inference.readthedocs.io/en/latest/user_guides/download_lib.html). You can view and select the appropriate version of the inference library on the official website.
+[Paddle inference library official website](https://paddle-inference.readthedocs.io/en/latest/user_guides/download_lib.html). You can review and select the appropriate version of the inference library on the official website.
-* After downloading, use the following method to uncompress.
+* After downloading, use the following command to extract files.
```
tar -xf paddle_inference.tgz
```
-Finally you can see the following files in the folder of `paddle_inference/`.
+Finally you will see the the folder of `paddle_inference/` in the current path.
-#### 1.2.2 Compile from the source code
-* If you want to get the latest Paddle inference library features, you can download the latest code from Paddle github repository and compile the inference library from the source code. It is recommended to download the inference library with paddle version greater than or equal to 2.0.1.
-* You can refer to [Paddle inference library] (https://www.paddlepaddle.org.cn/documentation/docs/en/advanced_guide/inference_deployment/inference/build_and_install_lib_en.html) to get the Paddle source code from github, and then compile To generate the latest inference library. The method of using git to access the code is as follows.
+#### 1.2.2 Compile the inference source code
+* If you want to get the latest Paddle inference library features, you can download the latest code from Paddle GitHub repository and compile the inference library from the source code. It is recommended to download the inference library with paddle version greater than or equal to 2.0.1.
+* You can refer to [Paddle inference library] (https://www.paddlepaddle.org.cn/documentation/docs/en/advanced_guide/inference_deployment/inference/build_and_install_lib_en.html) to get the Paddle source code from GitHub, and then compile To generate the latest inference library. The method of using git to access the code is as follows.
```shell
@@ -100,7 +99,7 @@ git clone https://github.com/PaddlePaddle/Paddle.git
git checkout release/2.2
```
-* After entering the Paddle directory, the commands to compile the paddle inference library are as follows.
+* Enter the Paddle directory and run the following commands to compile the paddle inference library.
```shell
rm -rf build
@@ -133,14 +132,14 @@ build/paddle_inference_install_dir/
|-- version.txt
```
-Among them, `paddle` is the Paddle library required for C++ prediction later, and `version.txt` contains the version information of the current inference library.
+`paddle` is the Paddle library required for C++ prediction later, and `version.txt` contains the version information of the current inference library.
## 2. Compile and Run the Demo
### 2.1 Export the inference model
-* You can refer to [Model inference](../../doc/doc_ch/inference.md)，export the inference model. After the model is exported, assuming it is placed in the `inference` directory, the directory structure is as follows.
+* You can refer to [Model inference](../../doc/doc_ch/inference.md) and export the inference model. After the model is exported, assuming it is placed in the `inference` directory, the directory structure is as follows.
```
inference/
@@ -171,20 +170,28 @@ CUDA_LIB_DIR=your_cuda_lib_dir
CUDNN_LIB_DIR=your_cudnn_lib_dir
```
-`OPENCV_DIR` is the opencv installation path; `LIB_DIR` is the download (`paddle_inference` folder)
+`OPENCV_DIR` is the OpenCV installation path; `LIB_DIR` is the download (`paddle_inference` folder)
or the generated Paddle inference library path (`build/paddle_inference_install_dir` folder);
-`CUDA_LIB_DIR` is the cuda library file path, in docker; it is `/usr/local/cuda/lib64`; `CUDNN_LIB_DIR` is the cudnn library file path, in docker it is `/usr/lib/x86_64-linux-gnu/`.
+`CUDA_LIB_DIR` is the CUDA library file path, in docker; it is `/usr/local/cuda/lib64`; `CUDNN_LIB_DIR` is the cuDNN library file path, in docker it is `/usr/lib/x86_64-linux-gnu/`.
* After the compilation is completed, an executable file named `ppocr` will be generated in the `build` folder.
### Run the demo
-Execute the built executable file:
+Execute the built executable file:
```shell
./build/ppocr
[--param1] [--param2] [...]
```
-Here, `mode` is a required parameter，and the value range is ['det', 'rec', 'system'], representing using detection only, using recognition only and using the end-to-end system respectively. Specifically,
+`mode` is a required parameter，and the valid values are
+
+mode value | Model used
+-----|------
+det | Detection only
+rec | Recognition only
+system | End-to-end system
+
+Specifically,
##### 1. run det demo:
```shell
@@ -214,9 +221,9 @@ Here, `mode` is a required parameter，and the value range is ['det', 'rec', 'sy
--image_dir=../../doc/imgs/12.jpg
```
-More parameters are as follows,
+More parameters are as follows,
-- common parameters
+- Common parameters
|parameter|data type|default|meaning|
| --- | --- | --- | --- |
@@ -226,7 +233,7 @@ More parameters are as follows,
|cpu_math_library_num_threads|int|10|Number of threads when using CPU inference. When machine cores is enough, the large the value, the faster the inference speed|
|use_mkldnn|bool|true|Whether to use mkdlnn library|
-- detection related parameters
+- Detection related parameters
|parameter|data type|default|meaning|
| --- | --- | --- | --- |
@@ -238,7 +245,7 @@ More parameters are as follows,
|use_polygon_score|bool|false|Whether to use polygon box to calculate bbox score, false means to use rectangle box to calculate. Use rectangular box to calculate faster, and polygonal box more accurate for curved text area.|
|visualize|bool|true|Whether to visualize the results，when it is set as true, The prediction result will be save in the image file `./ocr_vis.png`.|
-- classifier related parameters
+- Classifier related parameters
|parameter|data type|default|meaning|
| --- | --- | --- | --- |
@@ -246,7 +253,7 @@ More parameters are as follows,
|cls_model_dir|string|-|Address of direction classifier inference model|
|cls_thresh|float|0.9|Score threshold of the direction classifier|
-- recogniton related parameters
+- Recognition related parameters
|parameter|data type|default|meaning|
| --- | --- | --- | --- |
@@ -265,4 +272,4 @@ The detection results will be shown on the screen, which is as follows.
### 2.3 Notes
-* Paddle2.0.0 inference model library is recommended for this toturial.
+* Paddle 2.0.0 inference model library is recommended for this tutorial.
diff --git a/deploy/docker/hubserving/README.md b/deploy/docker/hubserving/README.md
index d4db277ffbeaf1efba18c0caef550404e08f2e85..a9cfc898105de146b32cb44eddcb85ddfe74687b 100644
--- a/deploy/docker/hubserving/README.md
+++ b/deploy/docker/hubserving/README.md
@@ -1,9 +1,9 @@
English | [简体中文](README_cn.md)
## Introduction
-Many users hope package the PaddleOCR service into a docker image, so that it can be quickly released and used in the docker or k8s environment.
+Many users hope package the PaddleOCR service into a docker image, so that it can be quickly released and used in the docker or K8s environment.
-This page provides some standardized code to achieve this goal. You can quickly publish the PaddleOCR project into a callable Restful API service through the following steps. (At present, the deployment based on the HubServing mode is implemented first, and author plans to increase the deployment of the PaddleServing mode in the futrue)
+This page provides some standardized code to achieve this goal. You can quickly publish the PaddleOCR project into a callable Restful API service through the following steps. (At present, the deployment based on the HubServing mode is implemented first, and author plans to increase the deployment of the PaddleServing mode in the future)
## 1. Prerequisites
@@ -14,7 +14,7 @@ c. NVIDIA Container Toolkit（GPU，Docker 19.03+ can skip this）
d. cuDNN 7.6+（GPU）
## 2. Build Image
-a. Goto Dockerfile directory（ps：Need to distinguish between cpu and gpu version, the following takes cpu as an example, gpu version needs to replace the keyword）
+a. Go to Dockerfile directory（PS: Need to distinguish between CPU and GPU version, the following takes CPU as an example, GPU version needs to replace the keyword）
```
cd deploy/docker/hubserving/cpu
```
@@ -42,13 +42,13 @@ docker logs -f paddle_ocr
```
## 4. Test
-a. Calculate the Base64 encoding of the picture to be recognized (if you just test, you can use a free online tool, like：https://freeonlinetools24.com/base64-image/）
+a. Calculate the Base64 encoding of the picture to be recognized (For test purpose, you can use a free online tool such as https://freeonlinetools24.com/base64-image/ )
b. Post a service request（sample request in sample_request.txt）
```
curl -H "Content-Type:application/json" -X POST --data "{\"images\": [\"Input image Base64 encode(need to delete the code 'data:image/jpg;base64,'）\"]}" http://localhost:8868/predict/ocr_system
```
-c. Get resposne（If the call is successful, the following result will be returned）
+c. Get response（If the call is successful, the following result will be returned）
```
{"msg":"","results":[[{"confidence":0.8403433561325073,"text":"约定","text_region":[[345,377],[641,390],[634,540],[339,528]]},{"confidence":0.8131805658340454,"text":"最终相遇","text_region":[[356,532],[624,530],[624,596],[356,598]]}]],"status":"0"}
```
diff --git a/deploy/lite/readme_en.md b/deploy/lite/readme_en.md
index 2c04c5673b1d7afec99d4e1219a20462c55be203..65dd10b710ed23ceb6ba01cd8f29b1bd01f1cf09 100644
--- a/deploy/lite/readme_en.md
+++ b/deploy/lite/readme_en.md
@@ -1,8 +1,8 @@
# Tutorial of PaddleOCR Mobile deployment
-This tutorial will introduce how to use [Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite) to deploy paddleOCR ultra-lightweight Chinese and English detection models on mobile phones.
+This tutorial will introduce how to use [Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite) to deploy PaddleOCR ultra-lightweight Chinese and English detection models on mobile phones.
-paddle-lite is a lightweight inference engine for PaddlePaddle. It provides efficient inference capabilities for mobile phones and IoTs, and extensively integrates cross-platform hardware to provide lightweight deployment solutions for end-side deployment issues.
+paddle-lite is a lightweight inference engine for PaddlePaddle. It provides efficient inference capabilities for mobile phones and IoT, and extensively integrates cross-platform hardware to provide lightweight deployment solutions for end-side deployment issues.
## 1. Preparation
diff --git a/deploy/pdserving/README_CN.md b/deploy/pdserving/README_CN.md
index 766c15181f2457b4a16a83a105cfa41de3c96cf3..2652ddeb86ee16549cbad3cd205e26cf4ea5f01b 100644
--- a/deploy/pdserving/README_CN.md
+++ b/deploy/pdserving/README_CN.md
@@ -22,6 +22,7 @@ PaddleOCR提供2种服务部署方式：
- [环境准备](#环境准备)
- [模型转换](#模型转换)
- [Paddle Serving pipeline部署](#部署)
+- [Windows用户](#Windows用户)
- [FAQ](#FAQ)
@@ -187,7 +188,8 @@ python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_rec_infer/ \
2021-05-13 03:42:36,979 chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0]
```
-## WINDOWS用户
+
+## Windows用户
Windows用户不能使用上述的启动方式，需要使用Web Service，详情参见[Windows平台使用Paddle Serving指导](https://github.com/PaddlePaddle/Serving/blob/develop/doc/Windows_Tutorial_CN.md)
diff --git a/deploy/slim/prune/README_en.md b/deploy/slim/prune/README_en.md
index fe9c5dcd6660757d11a884d4dbe077e95fad8afe..f0d652f249686c1d462cd2aa71f4766cf39e763e 100644
--- a/deploy/slim/prune/README_en.md
+++ b/deploy/slim/prune/README_en.md
@@ -28,14 +28,14 @@ python3 setup.py install
```
-### 2. Download Pretrain Model
+### 2. Download Pre-trained Model
Model prune needs to load pre-trained models.
PaddleOCR also provides a series of [models](../../../doc/doc_en/models_list_en.md). Developers can choose their own models or use their own models according to their needs.
### 3. Pruning sensitivity analysis
- After the pre-training model is loaded, sensitivity analysis is performed on each network layer of the model to understand the redundancy of each network layer, and save a sensitivity file which named: sen.pickle. After that, user could load the sensitivity file via the [methods provided by PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/prune/sensitive.py#L221) and determining the pruning ratio of each network layer automatically. For specific details of sensitivity analysis, see：[Sensitivity analysis](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/tutorials/image_classification_sensitivity_analysis_tutorial.md)
+ After the pre-trained model is loaded, sensitivity analysis is performed on each network layer of the model to understand the redundancy of each network layer, and save a sensitivity file which named: sen.pickle. After that, user could load the sensitivity file via the [methods provided by PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/prune/sensitive.py#L221) and determining the pruning ratio of each network layer automatically. For specific details of sensitivity analysis, see：[Sensitivity analysis](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/tutorials/image_classification_sensitivity_analysis_tutorial.md)
The data format of sensitivity file：
sen.pickle(Dict){
'layer_weight_name_0': sens_of_each_ratio(Dict){'pruning_ratio_0': acc_loss, 'pruning_ratio_1': acc_loss}
@@ -47,7 +47,7 @@ PaddleOCR also provides a series of [models](../../../doc/doc_en/models_list_en.
'conv10_expand_weights': {0.1: 0.006509952684312718, 0.2: 0.01827734339798862, 0.3: 0.014528405644659832, 0.6: 0.06536008804270439, 0.8: 0.11798612250664964, 0.7: 0.12391408417493704, 0.4: 0.030615754498018757, 0.5: 0.047105205602406594}
'conv10_linear_weights': {0.1: 0.05113190831455035, 0.2: 0.07705573833558801, 0.3: 0.12096721757739311, 0.6: 0.5135061352930738, 0.8: 0.7908166677143281, 0.7: 0.7272187676899062, 0.4: 0.1819252083008504, 0.5: 0.3728054727792405}
}
- The function would return a dict after loading the sensitivity file. The keys of the dict are name of parameters in each layer. And the value of key is the information about pruning sensitivity of correspoding layer. In example, pruning 10% filter of the layer corresponding to conv10_expand_weights would lead to 0.65% degradation of model performance. The details could be seen at: [Sensitivity analysis](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/algo/algo.md#2-%E5%8D%B7%E7%A7%AF%E6%A0%B8%E5%89%AA%E8%A3%81%E5%8E%9F%E7%90%86)
+ The function would return a dict after loading the sensitivity file. The keys of the dict are name of parameters in each layer. And the value of key is the information about pruning sensitivity of corresponding layer. In example, pruning 10% filter of the layer corresponding to conv10_expand_weights would lead to 0.65% degradation of model performance. The details could be seen at: [Sensitivity analysis](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/algo/algo.md#2-%E5%8D%B7%E7%A7%AF%E6%A0%B8%E5%89%AA%E8%A3%81%E5%8E%9F%E7%90%86)
Enter the PaddleOCR root directory，perform sensitivity analysis on the model with the following command：
diff --git a/deploy/slim/quantization/README_en.md b/deploy/slim/quantization/README_en.md
index bf3e91d69a298a7aaa8da21c22c45e62713e50cf..4cafe5f44e48a479cf5b0e4209b8e335a7e4917d 100644
--- a/deploy/slim/quantization/README_en.md
+++ b/deploy/slim/quantization/README_en.md
@@ -1,7 +1,7 @@
## Introduction
-Generally, a more complex model would achive better performance in the task, but it also leads to some redundancy in the model.
+Generally, a more complex model would achieve better performance in the task, but it also leads to some redundancy in the model.
Quantization is a technique that reduces this redundancy by reducing the full precision data to a fixed number,
so as to reduce model calculation complexity and improve model inference performance.
@@ -31,14 +31,14 @@ python setup.py install
```
-### 2. Download Pretrain Model
-PaddleOCR provides a series of trained [models](../../../doc/doc_en/models_list_en.md).
+### 2. Download Pre-trained Model
+PaddleOCR provides a series of pre-trained [models](../../../doc/doc_en/models_list_en.md).
If the model to be quantified is not in the list, you need to follow the [Regular Training](../../../doc/doc_en/quickstart_en.md) method to get the trained model.
### 3. Quant-Aware Training
Quantization training includes offline quantization training and online quantization training.
-Online quantization training is more effective. It is necessary to load the pre-training model.
+Online quantization training is more effective. It is necessary to load the pre-trained model.
After the quantization strategy is defined, the model can be quantified.
The code for quantization training is located in `slim/quantization/quant.py`. For example, to train a detection model, the training instructions are as follows:
@@ -54,7 +54,7 @@ python deploy/slim/quantization/quant.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3
### 4. Export inference model
-After getting the model after pruning and finetuning we, can export it as inference_model for predictive deployment:
+Once we got the model after pruning and fine-tuning, we can export it as an inference model for the deployment of predictive tasks:
```bash
python deploy/slim/quantization/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_inference_dir=./output/quant_inference_model
diff --git a/doc/doc_ch/android_demo.md b/doc/doc_ch/android_demo.md
index b6d9a7921b950a5a3c911802f0fa0ad40ab4d850..3b12308257c998387d0a95a46bcfdc7d8837caaf 100644
--- a/doc/doc_ch/android_demo.md
+++ b/doc/doc_ch/android_demo.md
@@ -14,12 +14,12 @@ Demo测试的时候使用的是NDK 20b版本，20版本以上均可以支持编
1. Start a new Android Studio project
- 在项目模版中选择 Native C++ 选择PaddleOCR/depoly/android_demo 路径
+ 在项目模版中选择 Native C++ 选择PaddleOCR/deploy/android_demo 路径
进入项目后会自动编译，第一次编译会花费较长的时间，建议添加代理加速下载。
**代理添加：**
-选择 Android Studio -> Perferences -> Appearance & Behavior -> System Settings -> HTTP Proxy -> Manual proxy configuration
+选择 Android Studio -> Preferences -> Appearance & Behavior -> System Settings -> HTTP Proxy -> Manual proxy configuration
diff --git a/doc/doc_ch/knowledge_distillation.md b/doc/doc_ch/knowledge_distillation.md
index 0923fc9ccc03053bd0d2e6ba0606dc980145c917..6eaaf3099f0e38ba4e99aea455a6d671393ffc6d 100644
--- a/doc/doc_ch/knowledge_distillation.md
+++ b/doc/doc_ch/knowledge_distillation.md
@@ -37,7 +37,7 @@
无论是大模型蒸馏小模型，还是小模型之间互相学习，更新参数，他们本质上是都是不同模型之间输出或者特征图(feature map)之间的相互监督，区别仅在于 (1) 模型是否需要固定参数。(2) 模型是否需要加载预训练模型。
-对于大模型蒸馏小模型的情况，大模型一般需要加载预训练模型并固定参数；对于小模型之间互相蒸馏的情况，小模型一般都不加载预训练模型，参数也都是可学习的状态。
+对于大模型蒸馏小模型的情况，大模型一般需要加载预训练模型并固定参数；对于小模型之间互相蒸馏的情况，小模型一般都不加载预训练模型，参数也都是可学习的状态。
在知识蒸馏任务中，不只有2个模型之间进行蒸馏的情况，多个模型之间互相学习的情况也非常普遍。因此在知识蒸馏代码框架中，也有必要支持该种类别的蒸馏方法。
@@ -551,7 +551,7 @@ Metric:
- 采用ch_PP-OCRv2_det_cml.yml，采用cml蒸馏，同样Teacher模型设置为PaddleOCR提供的模型或者您训练好的大模型
- 采用ch_PP-OCRv2_det_dml.yml，采用DML的蒸馏，两个Student模型互蒸馏的方法，在PaddleOCR采用的数据集上大约有1.7%的精度提升。
-在具体finetune时，需要在网络结构的`pretrained`参数中设置要加载的预训练模型。
+在具体fine-tune时，需要在网络结构的`pretrained`参数中设置要加载的预训练模型。
在精度提升方面，cml的精度>dml的精度>distill蒸馏方法的精度。当数据量不足或者Teacher模型精度与Student精度相差不大的时候，这个结论或许会改变。
diff --git a/doc/doc_ch/thirdparty.md b/doc/doc_ch/thirdparty.md
index 7466a6edf40d533b95cb6124da9d347dbcf877a6..a4f13af6e4677ffc4f827de1cc63ddcb8cafbe70 100644
--- a/doc/doc_ch/thirdparty.md
+++ b/doc/doc_ch/thirdparty.md
@@ -67,17 +67,17 @@ PaddleOCR非常欢迎社区贡献以PaddleOCR为核心的各种服务、部署
如果您在使用PaddleOCR时遇到了代码bug、功能不符合预期等问题，可以为PaddleOCR贡献您的修改，其中：
-- Python代码规范可参考[附录1：Python代码规范](./code_and_doc.md/#附录1)。
+- Python代码规范可参考[附录1：Python代码规范](./code_and_doc.md#附录1)。
-- 提交代码前请再三确认不会引入新的bug，并在PR中描述优化点。如果该PR解决了某个issue，请在PR中连接到该issue。所有的PR都应该遵守附录3中的[3.2.10 提交代码的一些约定。](./code_and_doc.md/#提交代码的一些约定)
+- 提交代码前请再三确认不会引入新的bug，并在PR中描述优化点。如果该PR解决了某个issue，请在PR中连接到该issue。所有的PR都应该遵守附录3中的[3.2.10 提交代码的一些约定。](./code_and_doc.md#提交代码的一些约定)
-- 请在提交之前参考下方的[附录3：Pull Request说明](./code_and_doc.md/#附录3)。如果您对git的提交流程不熟悉，同样可以参考附录3的3.2节。
+- 请在提交之前参考下方的[附录3：Pull Request说明](./code_and_doc.md#附录3)。如果您对git的提交流程不熟悉，同样可以参考附录3的3.2节。
**最后请在PR的题目中加上标签`【third-party】` , 在说明中@Evezerest，拥有此标签的PR将会被高优处理**。
### 2.3 文档优化
-如果您在使用PaddleOCR时遇到了文档表述不清楚、描述缺失、链接失效等问题，可以为PaddleOCR贡献您的修改。文档书写规范请参考[附录2：文档规范](./code_and_doc.md/#附录2)。**最后请在PR的题目中加上标签`【third-party】` , 在说明中@Evezerest，拥有此标签的PR将会被高优处理。**
+如果您在使用PaddleOCR时遇到了文档表述不清楚、描述缺失、链接失效等问题，可以为PaddleOCR贡献您的修改。文档书写规范请参考[附录2：文档规范](./code_and_doc.md#附录2)。**最后请在PR的题目中加上标签`【third-party】` , 在说明中@Evezerest，拥有此标签的PR将会被高优处理。**
## 3. 更多贡献机会
diff --git a/doc/doc_ch/update.md b/doc/doc_ch/update.md
index de5cdaf2aa24aa4c32e81001cdccec1156ee8605..c4c870681c6ccb5ad7702101312e5dbe47e9cb85 100644
--- a/doc/doc_ch/update.md
+++ b/doc/doc_ch/update.md
@@ -9,7 +9,7 @@
- 2020.12.07 [FAQ](../../doc/doc_ch/FAQ.md)新增5个高频问题，总数124个，并且计划以后每周一都会更新，欢迎大家持续关注。
- 2020.11.25 更新半自动标注工具[PPOCRLabel](../../PPOCRLabel/README_ch.md)，辅助开发者高效完成标注任务，输出格式与PP-OCR训练任务完美衔接。
- 2020.9.22 更新PP-OCR技术文章，https://arxiv.org/abs/2009.09941
-- 2020.9.19 更新超轻量压缩ppocr_mobile_slim系列模型，整体模型3.5M(详见PP-OCR Pipline)，适合在移动端部署使用。
+- 2020.9.19 更新超轻量压缩ppocr_mobile_slim系列模型，整体模型3.5M(详见PP-OCR Pipeline)，适合在移动端部署使用。
- 2020.9.17 更新超轻量ppocr_mobile系列和通用ppocr_server系列中英文ocr模型，媲美商业效果。
- 2020.9.17 更新[英文识别模型](./models_list.md#english-recognition-model)和[多语种识别模型](./models_list.md#english-recognition-model)，已支持`德语、法语、日语、韩语`，更多语种识别模型将持续更新。
- 2020.8.26 更新OCR相关的84个常见问题及解答，具体参考[FAQ](./FAQ.md)
diff --git a/doc/doc_en/FAQ_en.md b/doc/doc_en/FAQ_en.md
index 25777be77b6393c09c38e3c319ca1bd50cc3b1e8..5cf82a78720d15ce5b0aac37c409921474923813 100644
--- a/doc/doc_en/FAQ_en.md
+++ b/doc/doc_en/FAQ_en.md
@@ -1,7 +1,7 @@
## FAQ
1. **Prediction error: got an unexpected keyword argument 'gradient_clip'**
-The installed version of paddle is incorrect. Currently, this project only supports paddle1.7, which will be adapted to 1.8 in the near future.
+The installed version of paddle is incorrect. Currently, this project only supports Paddle 1.7, which will be adapted to 1.8 in the near future.
2. **Error when converting attention recognition model: KeyError: 'predict'**
Solved. Please update to the latest version of the code.
@@ -31,7 +31,7 @@ At present, PaddleOCR has opensourced two Chinese models, namely 8.6M ultra-ligh
|General Chinese OCR model|Resnet50_vd+Resnet34_vd|det_r50_vd_db.yml|rec_chinese_common_train.yml|
8. **Is there a plan to opensource a model that only recognizes numbers or only English + numbers?**
-It is not planned to opensource numbers only, numbers + English only, or other vertical text models. Paddleocr has opensourced a variety of detection and recognition algorithms for customized training. The two Chinese models are also based on the training output of the open-source algorithm library. You can prepare the data according to the tutorial, choose the appropriate configuration file, train yourselves, and we believe that you can get good result. If you have any questions during the training, you are welcome to open issues or ask in the communication group. We will answer them in time.
+It is not planned to opensource numbers only, numbers + English only, or other vertical text models. PaddleOCR has opensourced a variety of detection and recognition algorithms for customized training. The two Chinese models are also based on the training output of the open-source algorithm library. You can prepare the data according to the tutorial, choose the appropriate configuration file, train yourselves, and we believe that you can get good result. If you have any questions during the training, you are welcome to open issues or ask in the communication group. We will answer them in time.
9. **What is the training data used by the open-source model? Can it be opensourced?**
At present, the open source model, dataset and magnitude are as follows:
@@ -46,11 +46,11 @@ At present, the open source model, dataset and magnitude are as follows:
10. **Error in using the model with TPS module for prediction**
Error message: Input(X) dims[3] and Input(Grid) dims[2] should be equal, but received X dimension[3]\(108) != Grid dimension[2]\(100)
-Solution：TPS does not support variable shape. Please set --rec_image_shape='3,32,100' and --rec_char_type='en'
+Solution: TPS does not support variable shape. Please set --rec_image_shape='3,32,100' and --rec_char_type='en'
11. **Custom dictionary used during training, the recognition results show that words do not appear in the dictionary**
The used custom dictionary path is not set when making prediction. The solution is setting parameter `rec_char_dict_path` to the corresponding dictionary file.
12. **Results of cpp_infer and python_inference are very different**
-Versions of exprted inference model and inference libraray should be same. For example, on Windows platform, version of the inference libraray that PaddlePaddle provides is 1.8, but version of the inference model that PaddleOCR provides is 1.7, you should export model yourself(`tools/export_model.py`) on PaddlePaddle1.8 and then use the exported model for inference.
+Versions of exported inference model and inference library should be same. For example, on Windows platform, version of the inference library that PaddlePaddle provides is 1.8, but version of the inference model that PaddleOCR provides is 1.7, you should export model yourself(`tools/export_model.py`) on PaddlePaddle 1.8 and then use the exported model for inference.
diff --git a/doc/doc_en/android_demo_en.md b/doc/doc_en/android_demo_en.md
index bab4f5275c785d0dbbc1456ae29b98ff82cbae1a..fd962bb2911d952a4ec9919eebf1903daca323c7 100644
--- a/doc/doc_en/android_demo_en.md
+++ b/doc/doc_en/android_demo_en.md
@@ -20,7 +20,7 @@ File -> New ->New Project to create "Native C++" project
**Agent add:**
- Android Studio -> Perferences -> Appearance & Behavior -> System Settings -> HTTP Proxy -> Manual proxy configuration
+ Android Studio -> Preferences -> Appearance & Behavior -> System Settings -> HTTP Proxy -> Manual proxy configuration
diff --git a/doc/doc_en/angle_class_en.md b/doc/doc_en/angle_class_en.md
index b7fcd63e070318d3aab37714a1213ad9f56cb6fc..00888fb04b70753d3dbdd785b3d46f286aad0315 100644
--- a/doc/doc_en/angle_class_en.md
+++ b/doc/doc_en/angle_class_en.md
@@ -92,7 +92,7 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3,4,5,6,7' tools/train.py -c
PaddleOCR provides a variety of data augmentation methods. If you want to add disturbance during training, Please uncomment the `RecAug` and `RandAugment` fields under `Train.dataset.transforms` in the configuration file.
-The default perturbation methods are: cvtColor, blur, jitter, Gasuss noise, random crop, perspective, color reverse, RandAugment.
+The default perturbation methods are: cvtColor, blur, jitter, Gauss noise, random crop, perspective, color reverse, RandAugment.
Except for RandAugment, each disturbance method is selected with a 50% probability during the training process. For specific code implementation, please refer to:
[rec_img_aug.py](../../ppocr/data/imaug/rec_img_aug.py)
diff --git a/doc/doc_en/config_en.md b/doc/doc_en/config_en.md
index 9742c3d18ae52191106c5e08371cef1c09238bf6..eda1e13da956ab1eede72b97e62d76b915e02169 100644
--- a/doc/doc_en/config_en.md
+++ b/doc/doc_en/config_en.md
@@ -1,7 +1,7 @@
-# Configuration
+# Configuration
- [1. Optional Parameter List](#1-optional-parameter-list)
-- [2. Intorduction to Global Parameters of Configuration File](#2-intorduction-to-global-parameters-of-configuration-file)
+- [2. Introduction to Global Parameters of Configuration File](#2-introduction-to-global-parameters-of-configuration-file)
- [3. Multilingual Config File Generation](#3-multilingual-config-file-generation)
@@ -15,9 +15,9 @@ The following list can be viewed through `--help`
| -c | ALL | Specify configuration file to use | None | **Please refer to the parameter introduction for configuration file usage** |
| -o | ALL | set configuration options | None | Configuration using -o has higher priority than the configuration file selected with -c. E.g: -o Global.use_gpu=false |
-
+
-## 2. Intorduction to Global Parameters of Configuration File
+## 2. Introduction to Global Parameters of Configuration File
Take rec_chinese_lite_train_v2.0.yml as an example
### Global
@@ -30,7 +30,7 @@ Take rec_chinese_lite_train_v2.0.yml as an example
| print_batch_step | Set print log interval | 10 | \ |
| save_model_dir | Set model save path | output/{算法名称} | \ |
| save_epoch_step | Set model save interval | 3 | \ |
-| eval_batch_step | Set the model evaluation interval | 2000 or [1000, 2000] | runing evaluation every 2000 iters or evaluation is run every 2000 iterations after the 1000th iteration |
+| eval_batch_step | Set the model evaluation interval | 2000 or [1000, 2000] | running evaluation every 2000 iters or evaluation is run every 2000 iterations after the 1000th iteration |
| cal_metric_during_train | Set whether to evaluate the metric during the training process. At this time, the metric of the model under the current batch is evaluated | true | \ |
| load_static_weights | Set whether the pre-training model is saved in static graph mode (currently only required by the detection algorithm) | true | \ |
| pretrained_model | Set the path of the pre-trained model | ./pretrain_models/CRNN/best_accuracy | \ |
@@ -65,7 +65,7 @@ In PaddleOCR, the network is divided into four stages: Transform, Backbone, Neck
| Parameter | Use | Defaults | Note |
| :---------------------: | :---------------------: | :--------------: | :--------------------: |
| model_type | Network Type | rec | Currently support`rec`,`det`,`cls` |
-| algorithm | Model name | CRNN | See [algorithm_overview](./algorithm_overview.md) for the support list |
+| algorithm | Model name | CRNN | See [algorithm_overview](./algorithm_overview_en.md) for the support list |
| **Transform** | Set the transformation method | - | Currently only recognition algorithms are supported, see [ppocr/modeling/transform](../../ppocr/modeling/transform) for details |
| name | Transformation class name | TPS | Currently supports `TPS` |
| num_fiducial | Number of TPS control points | 20 | Ten on the top and bottom |
@@ -134,14 +134,14 @@ In PaddleOCR, the network is divided into four stages: Transform, Backbone, Neck
## 3. Multilingual Config File Generation
-PaddleOCR currently supports 80 (except Chinese) language recognition. A multi-language configuration file template is
+PaddleOCR currently supports recognition for 80 languages (besides Chinese). A multi-language configuration file template is
provided under the path `configs/rec/multi_languages`: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。
-There are two ways to create the required configuration file:：
+There are two ways to create the required configuration file:
1. Automatically generated by script
-[generate_multi_language_configs.py](../../configs/rec/multi_language/generate_multi_language_configs.py) Can help you generate configuration files for multi-language models
+Script [generate_multi_language_configs.py](../../configs/rec/multi_language/generate_multi_language_configs.py) can help you generate configuration files for multi-language models.
- Take Italian as an example, if your data is prepared in the following format:
```
@@ -196,21 +196,21 @@ Italian is made up of Latin letters, so after executing the command, you will ge
epoch_num: 500
...
character_dict_path: {path/of/dict} # path of dict
-
+
Train:
dataset:
name: SimpleDataSet
data_dir: train_data/ # root directory of training data
label_file_list: ["./train_data/train_list.txt"] # train label path
...
-
+
Eval:
dataset:
name: SimpleDataSet
data_dir: train_data/ # root directory of val data
label_file_list: ["./train_data/val_list.txt"] # val label path
...
-
+
```
diff --git a/doc/doc_en/customize_en.md b/doc/doc_en/customize_en.md
index fb47c14f3346e918f32950c8eec5ada76345ce59..80b8302238f61c6e6473d1e2359de30dbebcb929 100644
--- a/doc/doc_en/customize_en.md
+++ b/doc/doc_en/customize_en.md
@@ -22,7 +22,7 @@ For more details about data preparation and training tutorials, refer to the doc
PaddleOCR provides a concatenation tool for detection and recognition models, which can connect any trained detection model and any recognition model into a two-stage text recognition system. The input image goes through four main stages: text detection, text rectification, text recognition, and score filtering to output the text position and recognition results, and at the same time, you can choose to visualize the results.
-When performing prediction, you need to specify the path of a single image or a image folder through the parameter `image_dir`, the parameter `det_model_dir` specifies the path of detection model, and the parameter `rec_model_dir` specifies the path of recogniton model. The visualized results are saved to the `./inference_results` folder by default.
+When performing prediction, you need to specify the path of a single image or a image folder through the parameter `image_dir`, the parameter `det_model_dir` specifies the path of detection model, and the parameter `rec_model_dir` specifies the path of recognition model. The visualized results are saved to the `./inference_results` folder by default.
```
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/det/" --rec_model_dir="./inference/rec/"
diff --git a/doc/doc_en/detection_en.md b/doc/doc_en/detection_en.md
index 1be34b330f91b59060fc84af6f5ac44022da1a35..9f54dc06b9be16553518c301296e38e62cf1c8ec 100644
--- a/doc/doc_en/detection_en.md
+++ b/doc/doc_en/detection_en.md
@@ -4,7 +4,7 @@ This section uses the icdar2015 dataset as an example to introduce the training,
- [1. Data and Weights Preparation](#1-data-and-weights-preparatio)
* [1.1 Data Preparation](#11-data-preparation)
- * [1.2 Download Pretrained Model](#12-download-pretrained-model)
+ * [1.2 Download Pre-trained Model](#12-download-pretrained-model)
- [2. Training](#2-training)
* [2.1 Start Training](#21-start-training)
* [2.2 Load Trained Model and Continue Training](#22-load-trained-model-and-continue-training)
@@ -45,7 +45,7 @@ After decompressing the data set and downloading the annotation file, PaddleOCR/
└─ test_icdar2015_label.txt Test annotation of icdar dataset
```
-The provided annotation file format is as follow, seperated by "\t":
+The provided annotation file format is as follow, separated by "\t":
```
" Image file name Image annotation information encoded by json.dumps"
ch4_test_images/img_61.jpg [{"transcription": "MASA", "points": [[310, 104], [416, 141], [418, 216], [312, 179]]}, {...}]
@@ -59,10 +59,10 @@ 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.
-### 1.2 Download Pretrained Model
+### 1.2 Download Pre-trained 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 pre-trained 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 pre-trained 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).
```shell
cd PaddleOCR/
diff --git a/doc/doc_en/distributed_training.md b/doc/doc_en/distributed_training.md
index 519a42f0dc4b9bd4fa18f3f65019e4235282df92..2822ee5e4ea52720a458e4060d8a09be7b98846b 100644
--- a/doc/doc_en/distributed_training.md
+++ b/doc/doc_en/distributed_training.md
@@ -2,7 +2,7 @@
## Introduction
-The high performance of distributed training is one of the core advantages of PaddlePaddle. In the classification task, distributed training can achieve almost linear speedup ratio. Generally, OCR training task need massive training data. Such as recognition, ppocrv2.0 model is trained based on 1800W dataset, which is very time-consuming if using single machine. Therefore, the distributed training is used in paddleocr to speedup the training task. For more information about distributed training, please refer to [distributed training quick start tutorial](https://fleet-x.readthedocs.io/en/latest/paddle_fleet_rst/parameter_server/ps_quick_start.html).
+The high performance of distributed training is one of the core advantages of PaddlePaddle. In the classification task, distributed training can achieve almost linear speedup ratio. Generally, OCR training task need massive training data. Such as recognition, PP-OCR v2.0 model is trained based on 1800W dataset, which is very time-consuming if using single machine. Therefore, the distributed training is used in PaddleOCR to speedup the training task. For more information about distributed training, please refer to [distributed training quick start tutorial](https://fleet-x.readthedocs.io/en/latest/paddle_fleet_rst/parameter_server/ps_quick_start.html).
## Quick Start
@@ -35,7 +35,7 @@ python3 -m paddle.distributed.launch \
**Notice:**
* The IP addresses of different machines need to be separated by commas, which can be queried through `ifconfig` or `ipconfig`.
* Different machines need to be set to be secret free and can `ping` success with others directly, otherwise communication cannot establish between them.
-* The code, data and start command betweent different machines must be completely consistent and then all machines need to run start command. The first machine in the `ip_list` is set to `trainer0`, and so on.
+* The code, data and start command between different machines must be completely consistent and then all machines need to run start command. The first machine in the `ip_list` is set to `trainer0`, and so on.
## Performance comparison
diff --git a/doc/doc_en/environment_en.md b/doc/doc_en/environment_en.md
index 854db648fc255d36ec5c01c710a1d3cab53aaeb5..fc87f10c104628df0268bc6f8910c5914aeba225 100644
--- a/doc/doc_en/environment_en.md
+++ b/doc/doc_en/environment_en.md
@@ -4,9 +4,9 @@ Windows and Mac users are recommended to use Anaconda to build a Python environm
Recommended working environment:
- PaddlePaddle >= 2.0.0 (2.1.2)
-- python3.7
-- CUDA10.1 / CUDA10.2
-- CUDNN 7.6
+- Python 3.7
+- CUDA 10.1 / CUDA 10.2
+- cuDNN 7.6
* [1. Python Environment Setup](#1)
+ [1.1 Windows](#1.1)
@@ -25,7 +25,7 @@ Recommended working environment:
#### 1.1.1 Install Anaconda
-- Note: To use paddlepaddle you need to install python environment first, here we choose python integrated environment Anaconda toolkit
+- 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.
@@ -44,19 +44,19 @@ Recommended working environment:
- - Check conda to add environment variables and ignore the warning that
+ - Check Conda to add environment variables and ignore the warning that
-#### 1.1.2 Opening the terminal and creating the conda environment
+#### 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
+- Create a new Conda environment
```shell
# Enter the following command at the command line to create an environment named paddle_env
@@ -70,7 +70,7 @@ Recommended working environment:
-- To activate the conda environment you just created, enter the following command at the command line.
+- To activate the Conda environment you just created, enter the following command at the command line.
```shell
# Activate the paddle_env environment
@@ -91,7 +91,7 @@ The above anaconda environment and python environment are installed
#### 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
+- 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
@@ -108,17 +108,17 @@ The above anaconda environment and python environment are installed
- 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
+- It is recommended to install a code editor such as VSCode or PyCharm
-#### 1.2.2 Open a terminal and create a conda environment
+#### 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**
+- **Add Conda to the environment variables**
- - Environment variables are added so that the system can recognize the conda command
+ - Environment variables are added so that the system can recognize the Conda command
- Open `~/.bash_profile` in the terminal by typing the following command.
@@ -126,7 +126,7 @@ The above anaconda environment and python environment are installed
vim ~/.bash_profile
```
- - Add conda as an environment variable in `~/.bash_profile`.
+ - Add Conda as an environment variable in `~/.bash_profile`.
```shell
# Press i first to enter edit mode
@@ -156,12 +156,12 @@ The above anaconda environment and python environment are installed
- 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.
+ - 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
+ - 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
+- Create a new Conda environment
```shell
# Enter the following command at the command line to create an environment called paddle_env
@@ -175,7 +175,7 @@ The above anaconda environment and python environment are installed
- 
-- To activate the conda environment you just created, enter the following command at the command line.
+- To activate the Conda environment you just created, enter the following command at the command line.
```shell
# Activate the paddle_env environment
@@ -198,7 +198,7 @@ Linux users can choose to run either Anaconda or Docker. If you are familiar wit
#### 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
+- 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
@@ -214,9 +214,9 @@ Linux users can choose to run either Anaconda or Docker. If you are familiar wit
- 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 1: Download locally, then transfer the installation package to the Linux server
- - Download method 2: Directly use linux command line to download
+ - Download method 2: Directly use Linux command line to download
```shell
# First install wget
@@ -277,12 +277,12 @@ Linux users can choose to run either Anaconda or Docker. If you are familiar wit
- 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.
+ - 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
+ - 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
+- Create a new Conda environment
```shell
# Enter the following command at the command line to create an environment called paddle_env
@@ -296,7 +296,7 @@ Linux users can choose to run either Anaconda or Docker. If you are familiar wit
-- To activate the conda environment you just created, enter the following command at the command line.
+- To activate the Conda environment you just created, enter the following command at the command line.
```shell
# Activate the paddle_env environment
@@ -335,13 +335,13 @@ sudo docker container exec -it ppocr /bin/bash
## 2. Install PaddlePaddle 2.0
-- If you have cuda9 or cuda10 installed on your machine, please run the following command to install
+- If you have CUDA 9 or CUDA 10 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
+- If you have no available GPU on your machine, please run the following command to install the CPU version
```bash
python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
diff --git a/doc/doc_en/inference_en.md b/doc/doc_en/inference_en.md
index 019ac4d0ac15aceed89286048d2c4d88a259e501..a8a96e30f020b98b52bb465140b3463cd88beebb 100755
--- a/doc/doc_en/inference_en.md
+++ b/doc/doc_en/inference_en.md
@@ -139,7 +139,7 @@ tar xf ch_ppocr_mobile_v2.0_det_infer.tar
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:
+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:
@@ -244,7 +244,7 @@ The visualized text detection results are saved to the `./inference_results` fol
## 3. Text Recognition Model Inference
-The following will introduce the lightweight Chinese recognition model inference, other CTC-based and Attention-based text recognition models inference. For Chinese text recognition, it is recommended to choose the recognition model based on CTC loss. In practice, it is also found that the result of the model based on Attention loss is not as good as the one based on CTC loss. In addition, if the characters dictionary is modified during training, make sure that you use the same characters set during inferencing. Please check below for details.
+The following will introduce the lightweight Chinese recognition model inference, other CTC-based and Attention-based text recognition models inference. For Chinese text recognition, it is recommended to choose the recognition model based on CTC loss. In practice, it is also found that the result of the model based on Attention loss is not as good as the one based on CTC loss. In addition, if the characters dictionary is modified during training, make sure that you use the same characters set during inference. Please check below for details.
diff --git a/doc/doc_en/inference_ppocr_en.md b/doc/doc_en/inference_ppocr_en.md
index 62a672885c86119ae56dc93ef76c2bb746084a05..21f4c64d6dc99054a3480a66cd710b5d09461ca1 100755
--- a/doc/doc_en/inference_ppocr_en.md
+++ b/doc/doc_en/inference_ppocr_en.md
@@ -7,7 +7,7 @@ This article introduces the use of the Python inference engine for the PP-OCR mo
- [Text Detection Model Inference](#DETECTION_MODEL_INFERENCE)
- [Text Recognition Model Inference](#RECOGNITION_MODEL_INFERENCE)
- [1. Lightweight Chinese Recognition Model Inference](#LIGHTWEIGHT_RECOGNITION)
- - [2. Multilingaul Model Inference](#MULTILINGUAL_MODEL_INFERENCE)
+ - [2. Multilingual Model Inference](#MULTILINGUAL_MODEL_INFERENCE)
- [Angle Classification Model Inference](#ANGLE_CLASS_MODEL_INFERENCE)
- [Text Detection Angle Classification and Recognition Inference Concatenation](#CONCATENATION)
@@ -25,7 +25,7 @@ tar xf ch_PP-OCRv2_det_infer.tar
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv2_det_infer.tar/"
```
-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:
+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:
@@ -75,7 +75,7 @@ Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.9897658)
-### 2. Multilingaul Model Inference
+### 2. Multilingual Model Inference
If you need to predict [other language models](./models_list_en.md#Multilingual), 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:
diff --git a/doc/doc_en/installation_en.md b/doc/doc_en/installation_en.md
index dec384b2f27f8bb36ee67d8b040b532b30e0b028..bb499f5cd4b70da59445bec4f74ca4e0f4e517b1 100644
--- a/doc/doc_en/installation_en.md
+++ b/doc/doc_en/installation_en.md
@@ -1,17 +1,17 @@
## QUICK INSTALLATION
-After testing, paddleocr can run on glibc 2.23. You can also test other glibc versions or install glic 2.23 for the best compatibility.
+After testing, PaddleOCR can run on glibc 2.23. You can also test other glibc versions or install glibc 2.23 for the best compatibility.
PaddleOCR working environment:
- PaddlePaddle 2.0.0
-- python3.7
+- Python 3.7
- glibc 2.23
-It is recommended to use the docker provided by us to run PaddleOCR, please refer to the use of docker [link](https://www.runoob.com/docker/docker-tutorial.html/).
+It is recommended to use the docker provided by us to run PaddleOCR. Please refer to the docker tutorial [link](https://www.runoob.com/docker/docker-tutorial.html/).
-*If you want to directly run the prediction code on mac or windows, you can start from step 2.*
+*If you want to directly run the prediction code on Mac or Windows, you can start from step 2.*
-**1. (Recommended) Prepare a docker environment. The first time you use this docker image, it will be downloaded automatically. Please be patient.**
+**1. (Recommended) Prepare a docker environment. For the first time you use this docker image, it will be downloaded automatically. Please be patient.**
```
# Switch to the working directory
cd /home/Projects
@@ -22,7 +22,7 @@ cd /home/Projects
sudo docker run --name ppocr -v $PWD:/paddle --network=host -it paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82 /bin/bash
```
-If using CUDA10, please run the following command to create a container.
+With CUDA10, please run the following command to create a container.
It is recommended to set a shared memory greater than or equal to 32G through the --shm-size parameter:
```
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
@@ -51,11 +51,11 @@ For more software version requirements, please refer to the instructions in [Ins
# Recommend
git clone https://github.com/PaddlePaddle/PaddleOCR
-# If you cannot pull successfully due to network problems, you can also choose to use the code hosting on the cloud:
+# If you cannot pull successfully due to network problems, you can switch to the mirror hosted on Gitee:
git clone https://gitee.com/paddlepaddle/PaddleOCR
-# Note: The cloud-hosting code may not be able to synchronize the update with this GitHub project in real time. There might be a delay of 3-5 days. Please give priority to the recommended method.
+# Note: The mirror on Gitee may not keep in synchronization with the latest update with the project on GitHub. There might be a delay of 3-5 days. Please try GitHub at first.
```
**4. Install third-party libraries**
@@ -66,6 +66,6 @@ pip3 install -r requirements.txt
If you getting this error `OSError: [WinError 126] The specified module could not be found` when you install shapely on windows.
-Please try to download Shapely whl file using [http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely](http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely).
+Please try to download Shapely whl file from [http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely](http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely).
Reference: [Solve shapely installation on windows](https://stackoverflow.com/questions/44398265/install-shapely-oserror-winerror-126-the-specified-module-could-not-be-found)
diff --git a/doc/doc_en/models_en.md b/doc/doc_en/models_en.md
index 37c4a174563abc68085a103e11e2ddb3bd954714..d60ca2a3329f0c1b1ad0bfe6f200d0d5e63dcdaa 100644
--- a/doc/doc_en/models_en.md
+++ b/doc/doc_en/models_en.md
@@ -7,13 +7,13 @@ This section contains two parts. Firstly, [PP-OCR Model Download](./models_list_
Let's first understand some basic concepts.
-- [Introduction about OCR](#introduction-about-ocr)
+- [Introduction to OCR](#introduction-to-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)
-## 1. Introduction about OCR
+## 1. Introduction to OCR
This section briefly introduces the basic concepts of OCR detection model and recognition model, and introduces PaddleOCR's PP-OCR model.
diff --git a/doc/doc_en/models_list_en.md b/doc/doc_en/models_list_en.md
index e3cf251c3439ba009a1de0ba48f7c0aa10b117c4..b07d5f0a9471a1bdff7a93328d63e874fed60e19 100644
--- a/doc/doc_en/models_list_en.md
+++ b/doc/doc_en/models_list_en.md
@@ -1,6 +1,6 @@
# OCR Model List（V2.1, updated on 2021.9.6）
> **Note**
-> 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.
+> 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 has optimizations in accuracy and speed with CPU.
> 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).
@@ -18,7 +18,7 @@ The downloadable models provided by PaddleOCR include `inference model`, `traine
|--- | --- | --- |
|inference model|inference.pdmodel、inference.pdiparams|Used for inference based on Paddle inference engine，[detail](./inference_en.md)|
|trained model, pre-trained model|\*.pdparams、\*.pdopt、\*.states |The checkpoints model saved in the training process, which stores the parameters of the model, mostly used for model evaluation and continuous training.|
-|slim model|\*.nb| Model compressed by PaddleSim (a model compression tool using PaddlePaddle), which is suitable for mobile-side deployment scenarios (Paddle-Lite is needed for slim model deployment). |
+|slim model|\*.nb| Model compressed by PaddleSlim (a model compression tool using PaddlePaddle), which is suitable for mobile-side deployment scenarios (Paddle-Lite is needed for slim model deployment). |
Relationship of the above models is as follows.
@@ -50,7 +50,7 @@ Relationship of the above models is as follows.
|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) |
-**Note:** The `trained model` is finetuned on the `pre-trained model` with real data and synthsized vertical text data, which achieved better performance in real scene. The `pre-trained model` is directly trained on the full amount of real data and synthsized data, which is more suitable for finetune on your own dataset.
+**Note:** The `trained model` is fine-tuned on the `pre-trained model` with real data and synthesized vertical text data, which achieved better performance in real scene. The `pre-trained model` is directly trained on the full amount of real data and synthesized data, which is more suitable for fine-tune on your own dataset.
### 2.2 English Recognition Model
diff --git a/doc/doc_en/multi_languages_en.md b/doc/doc_en/multi_languages_en.md
index 545be5524f2c52c9799d3b013f1aac8baf1a379f..9f09b531d9f6f9912b69804e57cf4e78f0c15531 100644
--- a/doc/doc_en/multi_languages_en.md
+++ b/doc/doc_en/multi_languages_en.md
@@ -28,12 +28,12 @@ The multilingual models cover Latin, Arabic, Traditional Chinese, Korean, Japane
This document will briefly introduce how to use the multilingual model.
- [1 Installation](#Install)
- - [1.1 paddle installation](#paddleinstallation)
- - [1.2 paddleocr package installation](#paddleocr_package_install)
+ - [1.1 Paddle installation](#paddleinstallation)
+ - [1.2 PaddleOCR package installation](#paddleocr_package_install)
- [2 Quick Use](#Quick_Use)
- [2.1 Command line operation](#Command_line_operation)
- - [2.2 python script running](#python_Script_running)
+ - [2.2 Run with Python script](#python_Script_running)
- [3 Custom Training](#Custom_Training)
- [4 Inference and Deployment](#inference)
- [4 Supported languages and abbreviations](#language_abbreviations)
@@ -42,7 +42,7 @@ This document will briefly introduce how to use the multilingual model.
## 1 Installation
-### 1.1 paddle installation
+### 1.1 Paddle installation
```
# cpu
pip install paddlepaddle
@@ -52,7 +52,7 @@ pip install paddlepaddle-gpu
```
-### 1.2 paddleocr package installation
+### 1.2 PaddleOCR package installation
pip install
@@ -79,8 +79,8 @@ paddleocr -h
* Whole image prediction (detection + recognition)
-Paddleocr currently supports 80 languages, which can be switched by modifying the --lang parameter.
-The specific supported [language] (#language_abbreviations) can be viewed in the table.
+PaddleOCR currently supports 80 languages, which can be specified by the --lang parameter.
+The supported languages are listed in the [table](#language_abbreviations).
``` bash
paddleocr --image_dir doc/imgs_en/254.jpg --lang=en
@@ -90,7 +90,7 @@ paddleocr --image_dir doc/imgs_en/254.jpg --lang=en
-The result is a list, each item contains a text box, text and recognition confidence
+The result is a list. Each item contains a text box, text and recognition confidence
```text
[('PHO CAPITAL', 0.95723116), [[66.0, 50.0], [327.0, 44.0], [327.0, 76.0], [67.0, 82.0]]]
[('107 State Street', 0.96311164), [[72.0, 90.0], [451.0, 84.0], [452.0, 116.0], [73.0, 121.0]]]
@@ -110,7 +110,7 @@ paddleocr --image_dir doc/imgs_words_en/word_308.png --det false --lang=en
-The result is a tuple, which returns the recognition result and recognition confidence
+The result is a 2-tuple, which contains the recognition result and recognition confidence
```text
(0.99879867, 'LITTLE')
@@ -122,7 +122,7 @@ The result is a tuple, which returns the recognition result and recognition conf
paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --rec false
```
-The result is a list, each item contains only text boxes
+The result is a list. Each item represents the coordinates of a text box.
```
[[26.0, 457.0], [137.0, 457.0], [137.0, 477.0], [26.0, 477.0]]
@@ -132,9 +132,9 @@ The result is a list, each item contains only text boxes
```
@@ -18,11 +18,11 @@ PaddleOCR contains rich text detection, text recognition and end-to-end algorith
# Recommend
git clone https://github.com/PaddlePaddle/PaddleOCR
-# If you cannot pull successfully due to network problems, you can also choose to use the code hosting on the cloud:
+# If you cannot pull successfully due to network problems, you can switch to the mirror hosted on Gitee:
git clone https://gitee.com/paddlepaddle/PaddleOCR
-# Note: The cloud-hosting code may not be able to synchronize the update with this GitHub project in real time. There might be a delay of 3-5 days. Please give priority to the recommended method.
+# Note: The mirror on Gitee may not keep in synchronization with the latest project on GitHub. There might be a delay of 3-5 days. Please try GitHub at first.
```
### **2.2 Install third-party libraries**
@@ -34,6 +34,6 @@ pip3 install -r requirements.txt
If you getting this error `OSError: [WinError 126] The specified module could not be found` when you install shapely on windows.
-Please try to download Shapely whl file using [http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely](http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely).
+Please try to download Shapely whl file from [http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely](http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely).
-Reference: [Solve shapely installation on windows](https://stackoverflow.com/questions/44398265/install-shapely-oserror-winerror-126-the-specified-module-could-not-be-found)
\ No newline at end of file
+Reference: [Solve shapely installation on windows](https://stackoverflow.com/questions/44398265/install-shapely-oserror-winerror-126-the-specified-module-could-not-be-found)
diff --git a/doc/doc_en/pgnet_en.md b/doc/doc_en/pgnet_en.md
index e176a1260c734974e2dad843faeb3e5532176629..c7cb3221ccfd897e2fd9062a828c2fe0ceb42024 100644
--- a/doc/doc_en/pgnet_en.md
+++ b/doc/doc_en/pgnet_en.md
@@ -6,18 +6,18 @@
## 1. Brief Introduction
-OCR algorithm can be divided into two-stage algorithm and end-to-end algorithm. The two-stage OCR algorithm is generally divided into two parts, text detection and text recognition algorithm. The text detection algorithm gets the detection box of the text line from the image, and then the recognition algorithm identifies the content of the text box. The end-to-end OCR algorithm can complete text detection and recognition in one algorithm. Its basic idea is to design a model with both detection unit and recognition module, share the CNN features of both and train them together. Because one algorithm can complete character recognition, the end-to-end model is smaller and faster.
+OCR algorithms can be divided into two categories: two-stage algorithm and end-to-end algorithm. The two-stage OCR algorithm is generally divided into two parts, text detection and text recognition algorithm. The text detection algorithm locates the box of the text line from the image, and then the recognition algorithm identifies the content of the text box. The end-to-end OCR algorithm combines text detection and recognition in one algorithm. Its basic idea is to design a model with both detection unit and recognition module, share the CNN features of both and train them together. Because one algorithm can complete character recognition, the end-to-end model is smaller and faster.
### Introduction Of PGNet Algorithm
-In recent years, the end-to-end OCR algorithm has been well developed, including MaskTextSpotter series, TextSnake, TextDragon, PGNet series and so on. Among these algorithms, PGNet algorithm has the advantages that other algorithms do not
-- Pgnet loss is designed to guide training, and no character-level annotations is needed
-- NMS and ROI related operations are not needed, It can accelerate the prediction
+During the recent years, the end-to-end OCR algorithm has been well developed, including MaskTextSpotter series, TextSnake, TextDragon, PGNet series and so on. Among these algorithms, PGNet algorithm has some advantages over the other algorithms.
+- PGNet loss is designed to guide training, and no character-level annotations is needed.
+- NMS and ROI related operations are not needed. It can accelerate the prediction
- The reading order prediction module is proposed
- A graph based modification module (GRM) is proposed to further improve the performance of model recognition
- Higher accuracy and faster prediction speed
-For details of PGNet algorithm, please refer to [paper](https://www.aaai.org/AAAI21Papers/AAAI-2885.WangP.pdf) ,The schematic diagram of the algorithm is as follows:
+For details of PGNet algorithm, please refer to [paper](https://www.aaai.org/AAAI21Papers/AAAI-2885.WangP.pdf). The schematic diagram of the algorithm is as follows:
-After feature extraction, the input image is sent to four branches: TBO module for text edge offset prediction, TCL module for text centerline prediction, TDO module for text direction offset prediction, and TCC module for text character classification graph prediction.
+After feature extraction, the input image is sent to four branches: TBO module for text edge offset prediction, TCL module for text center-line prediction, TDO module for text direction offset prediction, and TCC module for text character classification graph prediction.
The output of TBO and TCL can get text detection results after post-processing, and TCL, TDO and TCC are responsible for text recognition.
The results of detection and recognition are as follows:
@@ -40,7 +40,7 @@ Please refer to [Operation Environment Preparation](./environment_en.md) to conf
## 3. Quick Use
-### inference model download
+### Inference model download
This section takes the trained end-to-end model as an example to quickly use the model prediction. First, download the trained end-to-end inference model [download address](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/e2e_server_pgnetA_infer.tar)
```
mkdir inference && cd inference
@@ -131,7 +131,7 @@ python3 tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml -o Optimizer.base_lr=0.0
```
#### 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.
+If you would like to load trained model and continue the training again, you can specify the parameter `Global.checkpoints` as the model path to be loaded.
```shell
python3 tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.checkpoints=./your/trained/model
```
diff --git a/doc/doc_en/training_en.md b/doc/doc_en/training_en.md
index d013f5ac706a2a2b4a5b58ba0a6dff09ab0b4654..1a3165d0ab226d7cbeef356ee750594c759cfe23 100644
--- a/doc/doc_en/training_en.md
+++ b/doc/doc_en/training_en.md
@@ -12,15 +12,15 @@
* [4. FAQ](#3-faq)
-This article will introduce the basic concepts that need to be mastered during model training and the tuning methods during training.
+This article will introduce the basic concepts that is necessary for model training and tuning.
-At the same time, it will briefly introduce the components of the PaddleOCR model training data and how to prepare the data finetune model in the vertical scene.
+At the same time, it will briefly introduce the structure of the training data and how to prepare the data to fine-tune model in vertical scenes.
## 1. Yml Configuration
-The PaddleOCR model uses configuration files to manage network training and evaluation parameters. In the configuration file, you can set the model, optimizer, loss function, and pre- and post-processing parameters of the model. PaddleOCR reads these parameters from the configuration file, and then builds a complete training process to complete the model training. When optimized, the configuration can be completed by modifying the parameters in the configuration file, which is simple to use and convenient to modify.
+The PaddleOCR uses configuration files to control network training and evaluation parameters. In the configuration file, you can set the model, optimizer, loss function, and pre- and post-processing parameters of the model. PaddleOCR reads these parameters from the configuration file, and then builds a complete training process to train the model. Fine-tuning can also be completed by modifying the parameters in the configuration file, which is simple and convenient.
For the complete configuration file description, please refer to [Configuration File](./config_en.md)
@@ -28,13 +28,13 @@ For the complete configuration file description, please refer to [Configuration
## 2. Basic Concepts
-In the process of model training, some hyperparameters need to be manually adjusted to help the model obtain the optimal index at the least loss. Different data volumes may require different hyper-parameters. When you want to finetune your own data or tune the model effect, there are several parameter adjustment strategies for reference:
+During the model training process, some hyper-parameters can be manually specified to obtain the optimal result at the least cost. Different data volumes may require different hyper-parameters. When you want to fine-tune the model based on your own data, there are several parameter adjustment strategies for reference:
### 2.1 Learning Rate
-The learning rate is one of the important hyperparameters for training neural networks. It represents the step length of the gradient moving to the optimal solution of the loss function in each iteration.
-A variety of learning rate update strategies are provided in PaddleOCR, which can be modified through configuration files, for example:
+The learning rate is one of the most important hyper-parameters for training neural networks. It represents the step length of the gradient moving towards the optimal solution of the loss function in each iteration.
+A variety of learning rate update strategies are provided by PaddleOCR, which can be specified in configuration files. For example,
```
Optimizer:
@@ -46,16 +46,15 @@ Optimizer:
warmup_epoch: 5
```
-Piecewise stands for piecewise constant attenuation. Different learning rates are specified in different learning stages,
-and the learning rate is the same in each stage.
+`Piecewise` stands for piece-wise constant attenuation. Different learning rates are specified in different learning stages, and the learning rate stay the same in each stage.
-warmup_epoch means that in the first 5 epochs, the learning rate will gradually increase from 0 to base_lr. For all strategies, please refer to the code [learning_rate.py](../../ppocr/optimizer/learning_rate.py).
+`warmup_epoch` means that in the first 5 epochs, the learning rate will be increased gradually from 0 to base_lr. For all strategies, please refer to the code [learning_rate.py](../../ppocr/optimizer/learning_rate.py).
### 2.2 Regularization
-Regularization can effectively avoid algorithm overfitting. PaddleOCR provides L1 and L2 regularization methods.
-L1 and L2 regularization are the most commonly used regularization methods.
+Regularization can effectively avoid algorithm over-fitting. PaddleOCR provides L1 and L2 regularization methods.
+L1 and L2 regularization are the most widely used regularization methods.
L1 regularization adds a regularization term to the objective function to reduce the sum of absolute values of the parameters;
while in L2 regularization, the purpose of adding a regularization term is to reduce the sum of squared parameters.
The configuration method is as follows:
@@ -95,7 +94,7 @@ The current open source models, data sets and magnitudes are as follows:
- Chinese data set, LSVT street view data set crops the image according to the truth value, and performs position calibration, a total of 30w images. In addition, based on the LSVT corpus, 500w of synthesized data.
- Small language data set, using different corpora and fonts, respectively generated 100w synthetic data set, and using ICDAR-MLT as the verification set.
-Among them, the public data sets are all open source, users can search and download by themselves, or refer to [Chinese data set](./datasets.md), synthetic data is not open source, users can use open source synthesis tools to synthesize by themselves. Synthesis tools include [text_renderer](https://github.com/Sanster/text_renderer), [SynthText](https://github.com/ankush-me/SynthText), [TextRecognitionDataGenerator](https://github.com/Belval/TextRecognitionDataGenerator) etc.
+Among them, the public data sets are all open source, users can search and download by themselves, or refer to [Chinese data set](../doc_ch/datasets.md), synthetic data is not open source, users can use open source synthesis tools to synthesize by themselves. Synthesis tools include [text_renderer](https://github.com/Sanster/text_renderer), [SynthText](https://github.com/ankush-me/SynthText), [TextRecognitionDataGenerator](https://github.com/Belval/TextRecognitionDataGenerator) etc.
@@ -129,17 +128,17 @@ There are several experiences for reference when constructing the data set:
**Q**: How to choose a suitable network input shape when training CRNN recognition?
A: The general height is 32, the longest width is selected, there are two methods:
-
+
(1) Calculate the aspect ratio distribution of training sample images. The selection of the maximum aspect ratio considers 80% of the training samples.
-
+
(2) Count the number of texts in training samples. The selection of the longest number of characters considers the training sample that satisfies 80%. Then the aspect ratio of Chinese characters is approximately considered to be 1, and that of English is 3:1, and the longest width is estimated.
**Q**: During the recognition training, the accuracy of the training set has reached 90, but the accuracy of the verification set has been kept at 70, what should I do?
A: If the accuracy of the training set is 90 and the test set is more than 70, it should be over-fitting. There are two methods to try:
-
+
(1) Add more augmentation methods or increase the [probability] of augmented prob (https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/ppocr/data/imaug/rec_img_aug.py#L341), The default is 0.4.
-
+
(2) Increase the [l2 dcay value] of the system (https://github.com/PaddlePaddle/PaddleOCR/blob/a501603d54ff5513fc4fc760319472e59da25424/configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml#L47)
**Q**: When the recognition model is trained, loss can drop normally, but acc is always 0
diff --git a/doc/doc_en/update_en.md b/doc/doc_en/update_en.md
index 6a95b5be279d7a0b8a204cadd46b283b5eb26690..39fd936d1bd4e5f8d8535805f865792820ee1199 100644
--- a/doc/doc_en/update_en.md
+++ b/doc/doc_en/update_en.md
@@ -5,7 +5,7 @@
- 2021.8.3 released PaddleOCR v2.2, add a new structured documents analysis toolkit, i.e., [PP-Structure](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/ppstructure/README.md), support layout analysis and table recognition (One-key to export chart images to Excel files).
- 2021.4.8 release end-to-end text recognition algorithm [PGNet](https://www.aaai.org/AAAI21Papers/AAAI-2885.WangP.pdf) which is published in AAAI 2021. Find tutorial [here](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/pgnet_en.md)；release multi language recognition [models](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/multi_languages_en.md), support more than 80 languages recognition; especically, the performance of [English recognition model](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/models_list_en.md#English) is Optimized.
-- 2021.1.21 update more than 25+ multilingual recognition models [models list](./doc/doc_en/models_list_en.md), including：English, Chinese, German, French, Japanese，Spanish，Portuguese Russia Arabic and so on. Models for more languages will continue to be updated [Develop Plan](https://github.com/PaddlePaddle/PaddleOCR/issues/1048).
+- 2021.1.21 update more than 25+ multilingual recognition models [models list](./models_list_en.md), including：English, Chinese, German, French, Japanese，Spanish，Portuguese Russia Arabic and so on. Models for more languages will continue to be updated [Develop Plan](https://github.com/PaddlePaddle/PaddleOCR/issues/1048).
- 2020.12.15 update Data synthesis tool, i.e., [Style-Text](../../StyleText/README.md)，easy to synthesize a large number of images which are similar to the target scene image.
- 2020.11.25 Update a new data annotation tool, i.e., [PPOCRLabel](../../PPOCRLabel/README.md), which is helpful to improve the labeling efficiency. Moreover, the labeling results can be used in training of the PP-OCR system directly.
- 2020.9.22 Update the PP-OCR technical article, https://arxiv.org/abs/2009.09941
diff --git a/paddleocr.py b/paddleocr.py
index a530d7868523019920652ec4611832e00fc4da3a..7c40e08e9620f64dd8f5cc023173442d5e525515 100644
--- a/paddleocr.py
+++ b/paddleocr.py
@@ -347,8 +347,9 @@ class PaddleOCR(predict_system.TextSystem):
ocr with paddleocr
args：
img: img for ocr, support ndarray, img_path and list or ndarray
- det: use text detection or not, if false, only rec will be exec. default is True
- rec: use text recognition or not, if false, only det will be exec. default is True
+ det: use text detection or not. If false, only rec will be exec. Default is True
+ rec: use text recognition or not. If false, only det will be exec. Default is True
+ cls: use angle classifier or not. Default is True. If true, the text with rotation of 180 degrees can be recognized. If no text is rotated by 180 degrees, use cls=False to get better performance. Text with rotation of 90 or 270 degrees can be recognized even if cls=False.
"""
assert isinstance(img, (np.ndarray, list, str))
if isinstance(img, list) and det == True:
diff --git a/ppstructure/README.md b/ppstructure/README.md
index abcc5281aa24c56e6a3f22e377de816b0bd66712..1d201a7c6e54f6ed71be6d1872b7f4b226ad35ad 100644
--- a/ppstructure/README.md
+++ b/ppstructure/README.md
@@ -96,7 +96,7 @@ In PP-Structure, the image will be divided into 5 types of areas **text, title,
#### 6.1.1 Layout analysis
-Layout analysis classifies image by region, including the use of Python scripts of layout analysis tools, extraction of designated category detection boxes, performance indicators, and custom training layout analysis models. For details, please refer to [document](layout/README.md).
+Layout analysis classifies image by region, including the use of Python scripts of layout analysis tools, extraction of designated category detection boxes, performance indicators, and custom training layout analysis models. For details, please refer to [document](layout/README_en.md).
#### 6.1.2 Table recognition
diff --git a/ppstructure/docs/quickstart.md b/ppstructure/docs/quickstart.md
index 446c577ec39cf24dd4b8699558c633a1308fa444..6f21f07fd47b92e41e9ac14fef630f7169d789d4 100644
--- a/ppstructure/docs/quickstart.md
+++ b/ppstructure/docs/quickstart.md
@@ -39,7 +39,7 @@ paddleocr --image_dir=../doc/table/1.png --type=structure
* VQA
-coming soon
+请参考：[文档视觉问答](../vqa/README.md)。
@@ -74,7 +74,7 @@ im_show.save('result.jpg')
* VQA
-comming soon
+请参考：[文档视觉问答](../vqa/README.md)。
@@ -101,7 +101,7 @@ dict 里各个字段说明如下
* VQA
-comming soon
+请参考：[文档视觉问答](../vqa/README.md)。
@@ -116,9 +116,9 @@ comming soon
| model_name_or_path | VQA SER模型地址 | None |
| max_seq_length | VQA SER模型最大支持token长度 | 512 |
| label_map_path | VQA SER 标签文件地址 | ./vqa/labels/labels_ser.txt |
-| mode | pipeline预测模式，structure: 版面分析+表格识别; vqa: ser文档信息抽取 | structure |
+| mode | pipeline预测模式，structure: 版面分析+表格识别; VQA: SER文档信息抽取 | structure |
-大部分参数和paddleocr whl包保持一致，见 [whl包文档](../doc/doc_ch/whl.md)
+大部分参数和PaddleOCR whl包保持一致，见 [whl包文档](../../doc/doc_ch/whl.md)
运行完成后，每张图片会在`output`字段指定的目录下有一个同名目录，图片里的每个表格会存储为一个excel，图片区域会被裁剪之后保存下来，excel文件和图片名名为表格在图片里的坐标。
diff --git a/test_tipc/readme.md b/test_tipc/readme.md
index 8b2489f3445ddfa87c1e587d6da81992fdb90e64..7b7548cd7296760d4caec0ed741c47137d86ece1 100644
--- a/test_tipc/readme.md
+++ b/test_tipc/readme.md
@@ -68,14 +68,14 @@ test_tipc/
├── model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt # 测试Linux上c++预测的配置文件
├── model_linux_gpu_normal_normal_infer_python_jetson.txt # 测试Jetson上python预测的配置文件
├── train_linux_gpu_fleet_amp_infer_python_linux_gpu_cpu.txt # 测试Linux上多机多卡、混合精度训练和python预测的配置文件
- ├── ...
+ ├── ...
├── ch_ppocr_server_v2.0_det # ch_ppocr_server_v2.0_det模型的测试配置文件目录
- ├── ...
+ ├── ...
├── ch_ppocr_mobile_v2.0_rec # ch_ppocr_mobile_v2.0_rec模型的测试配置文件目录
- ├── ...
+ ├── ...
├── ch_ppocr_server_v2.0_det # ch_ppocr_server_v2.0_det模型的测试配置文件目录
- ├── ...
- ├── ...
+ ├── ...
+ ├── ...
├── results/ # 预先保存的预测结果，用于和实际预测结果进行精读比对
├── python_ppocr_det_mobile_results_fp32.txt # 预存的mobile版ppocr检测模型python预测fp32精度的结果
├── python_ppocr_det_mobile_results_fp16.txt # 预存的mobile版ppocr检测模型python预测fp16精度的结果
@@ -119,7 +119,7 @@ bash test_tipc/test_train_inference_python.sh configs/[model_name]/[params_file_
bash test_tipc/prepare.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_infer_python.txt 'lite_train_lite_infer'
# 运行测试
bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_infer_python.txt 'lite_train_lite_infer'
-```
+```
关于本示例命令的更多信息可查看[基础训练预测使用文档](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/test_tipc/docs/test_train_inference_python.md#22-%E5%8A%9F%E8%83%BD%E6%B5%8B%E8%AF%95)。
### 配置文件命名规范
@@ -136,9 +136,9 @@ bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ch_ppocr_mobil
## 4. 开始测试
-各功能测试中涉及混合精度、裁剪、量化等训练相关，及mkldnn、Tensorrt等多种预测相关参数配置，请点击下方相应链接了解更多细节和使用教程：
-- [test_train_inference_python 使用](docs/test_train_inference_python.md) ：测试基于Python的模型训练、评估、推理等基本功能，包括裁剪、量化、蒸馏。
+各功能测试中涉及混合精度、裁剪、量化等训练相关，及mkldnn、Tensorrt等多种预测相关参数配置，请点击下方相应链接了解更多细节和使用教程：
+- [test_train_inference_python 使用](docs/test_train_inference_python.md) ：测试基于Python的模型训练、评估、推理等基本功能，包括裁剪、量化、蒸馏。
- [test_inference_cpp 使用](docs/test_inference_cpp.md)：测试基于C++的模型推理。
- [test_serving 使用](docs/test_serving.md)：测试基于Paddle Serving的服务化部署功能。
-- [test_lite_arm_cpu_cpp 使用](docs/test_lite_arm_cpu_cpp.md)：测试基于Paddle-Lite的ARM CPU端c++预测部署功能。
+- [test_lite_arm_cpp 使用](docs/test_lite_arm_cpp.md)：测试基于Paddle-Lite的ARM CPU端c++预测部署功能。
- [test_paddle2onnx 使用](docs/test_paddle2onnx.md)：测试Paddle2ONNX的模型转化功能，并验证正确性。