Merge pull request #5205 from fanruinet/dygraph

Correct some spellings & links in documents.

README.md

@@ -33,17 +33,17 @@ PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools
 - [more](./doc/doc_en/update_en.md)
 
 ## Features
-- PP-OCR series of high-quality pre-trained models, comparable to commercial effects
+- PP-OCR - A series of high-quality pre-trained models, comparable to commercial products
     - Ultra lightweight PP-OCRv2 series models: detection (3.1M) + direction classifier (1.4M) + recognition 8.5M) = 13.0M
     - Ultra lightweight PP-OCR mobile series models: detection (3.0M) + direction classifier (1.4M) + recognition (5.0M) = 9.4M
     - General PP-OCR server series models: detection (47.1M) + direction classifier (1.4M) + recognition (94.9M) = 143.4M
     - Support Chinese, English, and digit recognition, vertical text recognition, and long text recognition
-    - Support multi-language recognition: about 80 languages like Korean, Japanese, German, French, etc
+    - Support multi-lingual recognition: about 80 languages like Korean, Japanese, German, French, etc
 - PP-Structure: a document structurize system
-    - support layout analysis and table recognition (support export to Excel)
-    - support key information extraction
-    - support DocVQA
-- Rich toolkits related to the OCR areas
+    - Support layout analysis and table recognition (support export to Excel)
+    - Support key information extraction
+    - Support DocVQA
+- Rich OCR toolkit
     - Semi-automatic data annotation tool, i.e., PPOCRLabel: support fast and efficient data annotation
     - Data synthesis tool, i.e., Style-Text: easy to synthesize a large number of images which are similar to the target scene image
 - Support user-defined training, provides rich predictive inference deployment solutions
@@ -62,7 +62,7 @@ The above pictures are the visualizations of the general ppocr_server model. For
 
 <a name="Community"></a>
 ## Community
-- Scan the QR code below with your Wechat, you can access to official technical exchange group. Look forward to your participation.
+- Scan the QR code below with your Wechat, you can join the official technical discussion group. Looking forward to your participation.
 
 <div align="center">
 <img src="https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/dygraph/doc/joinus.PNG"  width = "200" height = "200" />
@@ -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)

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)

deploy/cpp_infer/readme_en.md

 # 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 develop
 ```
 
-* 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 <mode> [--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.

deploy/docker/hubserving/README.md

 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"}
 ```

deploy/lite/readme_en.md

 # 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
 

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)
 
 <a name="环境准备"></a>
@@ -187,9 +188,10 @@ 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
+<a name="Windows用户"></a>
+## 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)
+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)
 
 **WINDOWS只能使用0.5.0版本的CPU模式**
 

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：

deploy/slim/quantization/README_en.md

 
 ## 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

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
 
 ![](../demo/proxy.png)
 

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。
 
@@ -550,7 +550,7 @@ 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。
 

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
 

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)

doc/doc_en/FAQ_en.md

 ## 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.

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
 
 ![](../demo/proxy.png)
 

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)

doc/doc_en/config_en.md

-# 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)
 
 <a name="1-optional-parameter-list"></a>
@@ -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 |
 
-<a name="2-intorduction-to-global-parameters-of-configuration-file"></a>
+<a name="2-introduction-to-global-parameters-of-configuration-file"></a>
 
-## 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
       ...
-   
+
    ```
 
 

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/"

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/

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

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:
 
     <img src="../install/windows/anaconda_install_folder.png" alt="install config" width="500" align=" left"/>
 
-  - Check conda to add environment variables and ignore the warning that
+  - Check Conda to add environment variables and ignore the warning that
 
     <img src="../install/windows/anaconda_install_env.png" alt="add conda to path" width="500" align="center"/>
 
 
-#### 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
 
   <img src="../install/windows/anaconda_prompt.png" alt="anaconda download" width="300" align="center"/>
 
 
-- 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:
 
   <img src="../install/windows/conda_new_env.png" alt="conda create" width="700" align="center"/>
 
-- 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
 
     - <img src="../install/mac/conda_create.png" alt="conda_create" width="600" align="center"/>
 
-- 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
 
     <img src="../install/linux/conda_create.png" alt="conda_create" width="500" align="center"/>
 
-- 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