Merge pull request #3896 from Evezerest/dygraph

Upgrade document structure

README.md

 English | [简体中文](README_ch.md)
 
+<p align="center">
+ <img src="./doc/PaddleOCR_log.png" align="middle" width = "600"/>
+<p align="center">
+
+
+------------------------------------------------------------------------------------------
+
+<p align="left">
+    <a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache%202-dfd.svg"></a>
+    <a href="https://github.com/PaddlePaddle/PaddleOCR/releases"><img src="https://img.shields.io/github/v/release/PaddlePaddle/PaddleOCR?color=ffa"></a>
+    <a href=""><img src="https://img.shields.io/badge/python-3.7+-aff.svg"></a>
+    <a href=""><img src="https://img.shields.io/badge/os-linux%2C%20win%2C%20mac-pink.svg"></a>
+    <a href=""><img src="https://img.shields.io/pypi/format/PaddleOCR?color=c77"></a>
+    <a href="https://github.com/PaddlePaddle/PaddleOCR/graphs/contributors"><img src="https://img.shields.io/github/contributors/PaddlePaddle/PaddleOCR?color=9ea"></a>
+    <a href="https://pypi.org/project/PaddleOCR/"><img src="https://img.shields.io/pypi/dm/PaddleOCR?color=9cf"></a>
+    <a href="https://github.com/PaddlePaddle/PaddleOCR/stargazers"><img src="https://img.shields.io/github/stars/PaddlePaddle/PaddleOCR?color=ccf"></a>
+</p>
+
 ## Introduction
+
 PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools that help users train better models and apply them into practice.
 
 ## Notice
@@ -9,6 +28,11 @@ PaddleOCR supports both dynamic graph and static graph programming paradigm
 - Static graph: develop branch
 
 **Recent updates**
+
+- PaddleOCR R&D team would like to share the released tools with developers, at 20:15 pm on August 4th, [Live Address](https://live.bilibili.com/21689802).
+- 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).
 - 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.
@@ -78,25 +102,29 @@ For more model downloads (including multiple languages), please refer to [PP-OCR
 For a new language request, please refer to [Guideline for new language_requests](#language_requests).
 
 ## Tutorials
-- [Installation](./doc/doc_en/installation_en.md)
 - [Quick Start](./doc/doc_en/quickstart_en.md)
-- [Code Structure](./doc/doc_en/tree_en.md)
-- Algorithm Introduction
-    - [Text Detection Algorithm](./doc/doc_en/algorithm_overview_en.md)
-    - [Text Recognition Algorithm](./doc/doc_en/algorithm_overview_en.md)
-    - [PP-OCR Pipeline](#PP-OCR-Pipeline)
-- Model Training/Evaluation
+- [PaddleOCR Overview and Installation](./doc/doc_en/paddleOCR_overview_en.md)
+- PP-OCR Industry Landing: from Training to Deployment
+    - [PP-OCR Model and Configuration](./doc/doc_en/models_and_config_en.md)
+        - [PP-OCR Model Download](./doc/doc_en/models_list_en.md)
+        - [Yml Configuration](./doc/doc_en/config_en.md)
+        - [Python Inference](./doc/doc_en/inference_en.md)
+    - [PP-OCR Training](./doc/doc_en/training_en.md)
         - [Text Detection](./doc/doc_en/detection_en.md)
         - [Text Recognition](./doc/doc_en/recognition_en.md)
         - [Direction Classification](./doc/doc_en/angle_class_en.md)
-    - [Yml Configuration](./doc/doc_en/config_en.md)
-- Inference and Deployment
-    - [Quick Inference Based on PIP](./doc/doc_en/whl_en.md)
+    - Inference and Deployment
         - [Python Inference](./doc/doc_en/inference_en.md)
         - [C++ Inference](./deploy/cpp_infer/readme_en.md)
         - [Serving](./deploy/pdserving/README.md)
         - [Mobile](./deploy/lite/readme_en.md)
         - [Benchmark](./doc/doc_en/benchmark_en.md)  
+- [PP-Structure: Information Extraction](./ppstructure/README.md)
+    - [Layout Parser](./ppstructure/layout/README.md)
+    - [Table Recognition](./ppstructure/table/README.md)
+- Academic Circles
+    - [Two-stage Algorithm](./doc/doc_en/algorithm_overview_en.md)
+    - [PGNet Algorithm](./doc/doc_en/algorithm_overview_en.md)
 - Data Annotation and Synthesis
     - [Semi-automatic Annotation Tool: PPOCRLabel](./PPOCRLabel/README.md)
     - [Data Synthesis Tool: Style-Text](./StyleText/README.md)

README_ch.md

 [English](README.md) | 简体中文
 
+<p align="center">
+ <img src="./doc/PaddleOCR_log.png" align="middle" width = "600"/>
+<p align="center">
+
+
+------------------------------------------------------------------------------------------
+
+<p align="left">
+    <a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache%202-dfd.svg"></a>
+    <a href="https://github.com/PaddlePaddle/PaddleOCR/releases"><img src="https://img.shields.io/github/v/release/PaddlePaddle/PaddleOCR?color=ffa"></a>
+    <a href=""><img src="https://img.shields.io/badge/python-3.7+-aff.svg"></a>
+    <a href=""><img src="https://img.shields.io/badge/os-linux%2C%20win%2C%20mac-pink.svg"></a>
+    <a href=""><img src="https://img.shields.io/pypi/format/PaddleOCR?color=c77"></a>
+    <a href="https://github.com/PaddlePaddle/PaddleOCR/graphs/contributors"><img src="https://img.shields.io/github/contributors/PaddlePaddle/PaddleOCR?color=9ea"></a>
+    <a href="https://pypi.org/project/PaddleOCR/"><img src="https://img.shields.io/pypi/dm/PaddleOCR?color=9cf"></a>
+    <a href="https://github.com/PaddlePaddle/PaddleOCR/stargazers"><img src="https://img.shields.io/github/stars/PaddlePaddle/PaddleOCR?color=ccf"></a>
+</p>
+
 ## 简介
+
 PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力使用者训练出更好的模型，并应用落地。
 ## 注意
 PaddleOCR同时支持动态图与静态图两种编程范式
-- 动态图版本：dygraph分支（默认），需将paddle版本升级至2.0.0（[快速安装](./doc/doc_ch/installation.md)）
+
+- 动态图版本：release/2.2（默认分支，开发分支为dygraph分支），需将paddle版本升级至2.0.0或以上版本（[快速安装](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/installation.md)）
+
 - 静态图版本：develop分支
 
 **近期更新**
-- 2021.4.8 release 2.1版本，新增AAAI 2021论文[端到端识别算法PGNet](./doc/doc_ch/pgnet.md)开源，[多语言模型](./doc/doc_ch/multi_languages.md)支持种类增加到80+。
-- 2021.2.1 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题，总数162个，每周一都会更新，欢迎大家持续关注。
-- 2021.1.21 更新多语言识别模型，目前支持语种超过27种，包括中文简体、中文繁体、英文、法文、德文、韩文、日文、意大利文、西班牙文、葡萄牙文、俄罗斯文、阿拉伯文等，后续计划可以参考[多语言研发计划](https://github.com/PaddlePaddle/PaddleOCR/issues/1048)
-- 2020.12.15 更新数据合成工具[Style-Text](./StyleText/README_ch.md)，可以批量合成大量与目标场景类似的图像，在多个场景验证，效果明显提升。
-- 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
-- [More](./doc/doc_ch/update.md)
-
 
+- PaddleOCR研发团队对最新发版内容技术深入解读，8月4日晚上20:15，[直播地址](https://live.bilibili.com/21689802)。
+- 2021.8.3 正式发布PaddleOCR v2.2，新增文档结构分析[PP-Structure](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/ppstructure/README_ch.md)工具包，支持版面分析与表格识别（含Excel导出）。
+- 2021.6.29 [FAQ](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/FAQ.md)新增5个高频问题，总数248个，每周一都会更新，欢迎大家持续关注。
+- 2021.4.8 release 2.1版本，新增AAAI 2021论文[端到端识别算法PGNet](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/pgnet.md)开源，[多语言模型](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/multi_languages.md)支持种类增加到80+。
+- 2021.2.8 正式发布PaddleOCRv2.0(branch release/2.0)并设置为推荐用户使用的默认分支. 发布的详细内容，请参考: https://github.com/PaddlePaddle/PaddleOCR/releases/tag/v2.0.0
+- 2021.1.26,28,29 PaddleOCR官方研发团队带来技术深入解读三日直播课，1月26日、28日、29日晚上19:30，[直播地址](https://live.bilibili.com/21689802)
+- [More](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/update.md)
 
 ## 特性
 
@@ -72,36 +92,39 @@ PaddleOCR同时支持动态图与静态图两种编程范式
 更多模型下载（包括多语言），可以参考[PP-OCR v2.0 系列模型下载](./doc/doc_ch/models_list.md)
 
 ## 文档教程
-- [快速安装](./doc/doc_ch/installation.md)
-- [中文OCR模型快速使用](./doc/doc_ch/quickstart.md)
-- [多语言OCR模型快速使用](./doc/doc_ch/multi_languages.md)
-- [代码组织结构](./doc/doc_ch/tree.md)
-- 算法介绍
-    - [文本检测](./doc/doc_ch/algorithm_overview.md)
-    - [文本识别](./doc/doc_ch/algorithm_overview.md)
-    - [PP-OCR Pipeline](#PP-OCR)
-    - [端到端PGNet算法](./doc/doc_ch/pgnet.md)
-- 模型训练/评估
+- [快速开始](./doc/doc_ch/quickstart.md)
+- [PaddleOCR全景图与安装](./doc/doc_ch/paddleOCR_overview.md)
+- PP-OCR产业落地：从训练到部署
+    - [PP-OCR模型与配置文件](./doc/doc_ch/models_and_config.md)
+        - [PP-OCR模型下载](./doc/doc_ch/models_list.md)
+        - [配置文件内容与生成](./doc/doc_ch/config.md)
+        - [模型库快速使用](./doc/doc_ch/inference.md)
+    - [PP-OCR模型训练](./doc/doc_ch/training.md)
         - [文本检测](./doc/doc_ch/detection.md)
         - [文本识别](./doc/doc_ch/recognition.md)
         - [方向分类器](./doc/doc_ch/angle_class.md)
-    - [yml参数配置文件介绍](./doc/doc_ch/config.md)
-- 预测部署
-    - [基于pip安装whl包快速推理](./doc/doc_ch/whl.md)
+    - PP-OCR模型推理部署
         - [基于Python脚本预测引擎推理](./doc/doc_ch/inference.md)
         - [基于C++预测引擎推理](./deploy/cpp_infer/readme.md)
         - [服务化部署](./deploy/pdserving/README_CN.md)
         - [端侧部署](./deploy/lite/readme.md)
         - [Benchmark](./doc/doc_ch/benchmark.md)
-- 数据集
-    - [通用中英文OCR数据集](./doc/doc_ch/datasets.md)
-    - [手写中文OCR数据集](./doc/doc_ch/handwritten_datasets.md)
-    - [垂类多语言OCR数据集](./doc/doc_ch/vertical_and_multilingual_datasets.md)
+- [PP-Structure信息提取](./ppstructure/README_ch.md)
+    - [版面分析](./ppstructure/layout/README_ch.md)
+    - [表格识别](./ppstructure/table/README_ch.md)
 - 数据标注与合成
     - [半自动标注工具PPOCRLabel](./PPOCRLabel/README_ch.md)
     - [数据合成工具Style-Text](./StyleText/README_ch.md)
     - [其它数据标注工具](./doc/doc_ch/data_annotation.md)
     - [其它数据合成工具](./doc/doc_ch/data_synthesis.md)
+- OCR学术圈
+    - [两阶段模型介绍与下载](./doc/doc_ch/algorithm_overview.md)
+    - [端到端PGNet算法](./doc/doc_ch/pgnet.md)
+    - 模型训练
+- 数据集
+    - [通用中英文OCR数据集](./doc/doc_ch/datasets.md)
+    - [手写中文OCR数据集](./doc/doc_ch/handwritten_datasets.md)
+    - [垂类多语言OCR数据集](./doc/doc_ch/vertical_and_multilingual_datasets.md)
 - [效果展示](#效果展示)
 - FAQ
     - [【精选】OCR精选10个问题](./doc/doc_ch/FAQ.md)
@@ -111,6 +134,7 @@ PaddleOCR同时支持动态图与静态图两种编程范式
 - [参考文献](./doc/doc_ch/reference.md)
 - [许可证书](#许可证书)
 - [贡献代码](#贡献代码)
+- [代码组织结构](./doc/doc_ch/tree.md)
 
 
 <a name="PP-OCR"></a>

doc/doc_ch/config.md

-## 可选参数列表
+# 配置文件内容与生成
+
+[toc]
+
+## 1. 可选参数列表
 
 以下列表可以通过`--help`查看
 
@@ -8,10 +12,10 @@
 |          -o              |      ALL       |  设置配置文件里的参数内容  |  None  |  使用-o配置相较于-c选择的配置文件具有更高的优先级。例如：`-o Global.use_gpu=false`  |  
 
 
-## 配置文件参数介绍
+## 2. 配置文件参数介绍
 
 以 `rec_chinese_lite_train_v2.0.yml ` 为例
-### Global
+### 2.1 Global
 
 |         字段             |            用途                |      默认值       |            备注            |
 | :----------------------: |  :---------------------:   | :--------------:  |   :--------------------:   |
@@ -121,3 +125,7 @@
 |      batch_size_per_card        |        训练时单卡batch size         |  256 | \  |
 |      drop_last        |        是否丢弃因数据集样本数不能被 batch_size 整除而产生的最后一个不完整的mini-batch        |  True | \  |
 |      num_workers        |        用于加载数据的子进程个数，若为0即为不开启子进程，在主进程中进行数据加载        |  8 | \  |
+
+## 3. 多语言配置文件生成
+
+【参考识别模型训练补充内容】

doc/doc_ch/environment.md

+# 零基础Python环境搭建
+
+## Windows
+
+### 第1步：安装Anaconda
+
+- 说明：使用paddlepaddle需要先安装python环境，这里我们选择python集成环境Anaconda工具包
+  - Anaconda是1个常用的python包管理程序
+  - 安装完Anaconda后，可以安装python环境，以及numpy等所需的工具包环境。
+- Anaconda下载：
+  - 地址：https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/?C=M&O=D
+  - 大部分win10电脑均为64位操作系统，选择x86_64版本；若电脑为32位操作系统，则选择x86.exe
+
+    <img src="../install/windows/Anaconda_download.png" alt="anaconda download" width="800" align="left"/>
+  - 下载完成后，双击安装程序进入图形界面
+  - 默认安装位置为C盘，建议将安装位置更改到D盘：
+
+    <img src="../install/windows/anaconda_install_folder.png" alt="install config" width="500" align="left"/>
+  - 勾选conda加入环境变量，忽略警告：
+
+    <img src="../install/windows/anaconda_install_env.png" alt="add conda to path" width="500" align="left"/>
+
+### 第2步：打开终端并创建conda环境
+
+- 打开Anaconda Prompt终端：左下角Windows Start Menu -> Anaconda3 -> Anaconda Prompt启动控制台
+
+  <img src="../install/windows/anaconda_prompt.png" alt="anaconda download" width="300" align="left"/>
+
+
+- 创建新的conda环境
+
+  ```shell
+  # 在命令行输入以下命令，创建名为paddle_env的环境
+  # 此处为加速下载，使用清华源
+  conda create --name paddle_env python=3.8 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/  # 这是一行命令
+  ```
+
+  该命令会创建1个名为paddle_env、python版本为3.8的可执行环境，根据网络状态，需要花费一段时间
+
+  之后命令行中会输出提示信息，输入y并回车继续安装
+
+  <img src="../install/windows/conda_new_env.png" alt="conda create" width="700" align="left"/>
+
+- 激活刚创建的conda环境，在命令行中输入以下命令：
+
+  ```shell
+  # 激活paddle_env环境
+  conda activate paddle_env
+  # 查看当前python的位置
+  where python
+  ```
+
+    <img src="../install/windows/conda_list_env.png" alt="create environment" width="600" align="left"/>
+  
+    
+  
+  
+  
+  
+
+以上anaconda环境和python环境安装完毕
+
+
+
+## Mac
+
+### 第1步：安装Anaconda
+
+- 说明：使用paddlepaddle需要先安装python环境，这里我们选择python集成环境Anaconda工具包
+  - Anaconda是1个常用的python包管理程序
+  - 安装完Anaconda后，可以安装python环境，以及numpy等所需的工具包环境
+- Anaconda下载：
+  - 地址：https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/?C=M&O=D
+  
+  <img src="../install/mac/anaconda_start.png" alt="anaconda download" width="800" align="left"/>
+  
+  - 选择最下方的`Anaconda3-2021.05-MacOSX-x86_64.pkg`下载
+- 下载完成后，双击.pkg文件进入图形界面
+  - 按默认设置即可，安装需要花费一段时间
+- 建议安装vscode或pycharm等代码编辑器
+
+### 第2步：打开终端并创建conda环境
+
+- 打开终端
+
+  - 同时按下command键和空格键，在聚焦搜索中输入"终端"，双击进入终端
+
+- **将conda加入环境变量**
+
+  - 加入环境变量是为了让系统能识别conda命令
+
+  - 输入以下命令，在终端中打开`~/.bash_profile`：
+
+     ```shell
+     vim ~/.bash_profile
+     ```
+
+  - 在`~/.bash_profile`中将conda添加为环境变量：
+
+     ```shell
+     # 先按i进入编辑模式
+     # 在第一行输入：
+     export PATH="~/opt/anaconda3/bin:$PATH"
+     # 若安装时自定义了安装位置，则将~/opt/anaconda3/bin改为自定义的安装目录下的bin文件夹
+     ```
+
+    ```shell
+    # 修改后的~/.bash_profile文件应如下（其中xxx为用户名）：
+    export PATH="~/opt/anaconda3/bin:$PATH"
+    # >>> conda initialize >>>
+    # !! Contents within this block are managed by 'conda init' !!
+    __conda_setup="$('/Users/xxx/opt/anaconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)"
+    if [ $? -eq 0 ]; then
+    		eval "$__conda_setup"
+    else
+    		if [ -f "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh" ]; then
+    				. "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh"
+    		else
+    				export PATH="/Users/xxx/opt/anaconda3/bin:$PATH"
+    		fi
+    fi
+    unset __conda_setup
+    # <<< conda initialize <<<
+    ```
+
+    - 修改完成后，先按`esc`键退出编辑模式，再输入`:wq!`并回车，以保存退出
+
+  - 验证是否能识别conda命令：
+
+    - 在终端中输入`source ~/.bash_profile`以更新环境变量
+    - 再在终端输入`conda info --envs`，若能显示当前有base环境，则conda已加入环境变量
+
+- 创建新的conda环境
+
+  ```shell
+  # 在命令行输入以下命令，创建名为paddle_env的环境
+  # 此处为加速下载，使用清华源
+  conda create --name paddle_env python=3.8 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
+  ```
+
+  - 该命令会创建1个名为paddle_env、python版本为3.8的可执行环境，根据网络状态，需要花费一段时间
+
+  - 之后命令行中会输出提示信息，输入y并回车继续安装
+
+    - <img src="../install/mac/conda_create.png" alt="conda_create" width="600" align="left"/>
+
+- 激活刚创建的conda环境，在命令行中输入以下命令：
+
+  ```shell
+  # 激活paddle_env环境
+  conda activate paddle_env
+  # 查看当前python的位置
+  where python
+  ```
+
+  <img src="../install/mac/conda_activate.png" alt="conda_actviate" width="600" align="left"/>
+
+以上anaconda环境和python环境安装完毕
+
+
+
+## Linux
+
+### 第1步：安装Anaconda
+
+- 说明：使用paddlepaddle需要先安装python环境，这里我们选择python集成环境Anaconda工具包
+  - Anaconda是1个常用的python包管理程序
+  - 安装完Anaconda后，可以安装python环境，以及numpy等所需的工具包环境
+  
+- **下载Anaconda**：
+
+  - 下载地址：https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/?C=M&O=D
+    <img src="../install/linux/anaconda_download.png" akt="anaconda download" width="800" align="left"/>
+
+    
+
+    
+
+    
+
+    
+
+    
+
+    
+
+    
+
+    
+
+    - 选择适合您操作系统的版本
+      - 可在终端输入`uname -m`查询系统所用的指令集
+
+  - 下载法1：本地下载，再将安装包传到linux服务器上
+
+  - 下载法2：直接使用linux命令行下载
+
+    ```shell
+    # 首先安装wget
+    sudo apt-get install wget  # Ubuntu
+    sudo yum install wget  # CentOS
+    ```
+
+    ```shell
+    # 然后使用wget从清华源上下载
+    # 如要下载Anaconda3-2021.05-Linux-x86_64.sh，则下载命令如下：
+    wget https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda3-2021.05-Linux-x86_64.sh
+    
+    # 若您要下载其他版本，需要将最后1个/后的文件名改成您希望下载的版本
+    ```
+
+- 安装Anaconda：
+
+  - 在命令行输入`sh Anaconda3-2021.05-Linux-x86_64.sh`
+    - 若您下载的是其它版本，则将该命令的文件名替换为您下载的文件名
+  - 按照安装提示安装即可
+    - 查看许可时可输入q来退出
+  
+- **将conda加入环境变量**
+
+  - 加入环境变量是为了让系统能识别conda命令，若您在安装时已将conda加入环境变量path，则可跳过本步
+
+  - 在终端中打开`~/.bashrc`：
+
+    ```shell
+    # 在终端中输入以下命令：
+    vim ~/.bashrc
+    ```
+
+  - 在`~/.bashrc`中将conda添加为环境变量：
+
+    ```shell
+    # 先按i进入编辑模式
+    # 在第一行输入：
+    export PATH="~/anaconda3/bin:$PATH"
+    # 若安装时自定义了安装位置，则将~/anaconda3/bin改为自定义的安装目录下的bin文件夹
+    ```
+
+     ```shell
+      # 修改后的~/.bash_profile文件应如下（其中xxx为用户名）：
+      export PATH="~/opt/anaconda3/bin:$PATH"
+      # >>> conda initialize >>>
+      # !! Contents within this block are managed by 'conda init' !!
+      __conda_setup="$('/Users/xxx/opt/anaconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)"
+      if [ $? -eq 0 ]; then
+          eval "$__conda_setup"
+      else
+          if [ -f "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh" ]; then
+              . "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh"
+          else
+              export PATH="/Users/xxx/opt/anaconda3/bin:$PATH"
+          fi
+      fi
+      unset __conda_setup
+      # <<< conda initialize <<<
+     ```
+
+    - 修改完成后，先按`esc`键退出编辑模式，再输入`:wq!`并回车，以保存退出
+
+  - 验证是否能识别conda命令：
+
+    - 在终端中输入`source ~/.bash_profile`以更新环境变量
+    - 再在终端输入`conda info --envs`，若能显示当前有base环境，则conda已加入环境变量
+
+### 第2步：创建conda环境
+
+- 创建新的conda环境
+
+   ```shell
+    # 在命令行输入以下命令，创建名为paddle_env的环境
+    # 此处为加速下载，使用清华源
+    conda create --name paddle_env python=3.8 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
+   ```
+
+  - 该命令会创建1个名为paddle_env、python版本为3.8的可执行环境，根据网络状态，需要花费一段时间
+
+  - 之后命令行中会输出提示信息，输入y并回车继续安装
+
+    <img src="../install/linux/conda_create.png" alt="conda_create" width="500" align="left"/>
+
+- 激活刚创建的conda环境，在命令行中输入以下命令：
+
+  ```shell
+    # 激活paddle_env环境
+    conda activate paddle_env
+  ```
+  
+
+以上anaconda环境和python环境安装完毕
\ No newline at end of file

doc/doc_ch/multi_languages.md

@@ -200,9 +200,9 @@ ppocr 支持使用自己的数据进行自定义训练或finetune, 其中识别
 |英文|english|en| |乌克兰文|Ukranian|uk|
 |法文|french|fr| |白俄罗斯文|Belarusian|be|
 |德文|german|german| |泰卢固文|Telugu |te|
-|日文|japan|japan| | |阿巴扎文|Abaza |abq|
+|日文|japan|japan| | 阿巴扎文 | Abaza        | abq  |
 |韩文|korean|korean| |泰米尔文|Tamil |ta|
-|中文繁体|chinese traditional |ch_tra| |南非荷兰文 |Afrikaans |af|
+|中文繁体|chinese traditional |chinese_cht| |南非荷兰文 |Afrikaans |af|
 |意大利文| Italian |it| |阿塞拜疆文 |Azerbaijani    |az|
 |西班牙文|Spanish |es| |波斯尼亚文|Bosnian|bs|
 |葡萄牙文| Portuguese|pt| |捷克文|Czech|cs|

doc/doc_ch/paddleOCR_overview.md

+# PaddleOCR全景图与项目克隆
+

doc/doc_ch/quickstart.md

+# PaddleOCR快速开始
+- [PaddleOCR快速开始](#paddleocr)
+  * [1. 轻量安装](#1)
+    + [1.0 运行环境准备](#10)
+    + [1.1 安装PaddlePaddle2.0](#11)
+    + [1.2 安装PaddleOCR whl包](#12)
+  * [2. 便捷使用](#2)
+    + [2.1 命令行使用](#21)
+      - [2.1.1 中英文模型](#211)
+      - [2.1.2 多语言模型](#212)
+      - [2.1.3 版面分析](#213)
+    + [2.2 Python脚本使用](#22)
+      - [2.2.1 中英文与多语言使用](#221)
+      - [2.2.2 版面分析使用](#222)
 
-# 中文OCR模型快速使用
 
-## 1.环境配置
+<a name="1"></a>
+## 1. 轻量安装
+<a name="10"></a>
+### 1.0 运行环境准备
 
-请先参考[快速安装](./installation.md)配置PaddleOCR运行环境。
+如果您未搭建过Python环境，可以通过[零基础Python环境搭建文档](./environment.)进行环境搭建
+<a name="11"></a>
+### 1.1 安装PaddlePaddle2.0
 
-*注意：也可以通过 whl 包安装使用PaddleOCR，具体参考[Paddleocr Package使用说明](./whl.md)。*
+- 如果您的机器安装的是CUDA9或CUDA10，请运行以下命令安装
 
-## 2.inference模型下载
+```bash
+python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
+```
 
-* 移动端和服务器端的检测与识别模型如下，更多模型下载（包括多语言），可以参考[PP-OCR v2.0 系列模型下载](../doc_ch/models_list.md)
+- 如果您的机器是CPU，请运行以下命令安装
 
-| 模型简介     | 模型名称     |推荐场景          | 检测模型 | 方向分类器 | 识别模型 |
-| ------------ | --------------- | ----------------|---- | ---------- | -------- |
-| 中英文超轻量OCR模型（8.1M） | ch_ppocr_mobile_v2.0_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar)      |
-| 中英文通用OCR模型（143M）   | ch_ppocr_server_v2.0_xx |服务器端 |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar)          |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar)    |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar)  |
+```bash
+python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
+```
 
+更多的版本需求，请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
+<a name="12"></a>
+### 1.2 安装PaddleOCR whl包
 
-* windows 环境下如果没有安装wget,下载模型时可将链接复制到浏览器中下载，并解压放置在相应目录下
+```bash
+pip install "paddleocr>=2.0.1" # 推荐使用2.0.1+版本
+```
 
-复制上表中的检测和识别的`inference模型`下载地址，并解压
+- 对于Windows环境用户：
+
+  直接通过pip安装的shapely库可能出现`[winRrror 126] 找不到指定模块的问题`。建议从[这里](https://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely)下载shapely安装包完成安装，
+
+- 使用**版面分析**功能时，运行以下命令**安装 Layout-Parser**
+
+  ```bash
+  pip3 install -U https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
+  ```
 
-```
-mkdir inference && cd inference
-# 下载检测模型并解压
-wget {url/of/detection/inference_model} && tar xf {name/of/detection/inference_model/package}
-# 下载识别模型并解压
-wget {url/of/recognition/inference_model} && tar xf {name/of/recognition/inference_model/package}
-# 下载方向分类器模型并解压
-wget {url/of/classification/inference_model} && tar xf {name/of/classification/inference_model/package}
-cd ..
-```
 
-以超轻量级模型为例：
+<a name="2"></a>
+## 2. 便捷使用
+<a name="21"></a>
+### 2.1 命令行使用
+
+PaddleOCR提供了一系列测试图片，点击xx下载，然后在终端中切换到相应目录
 
 ```
-mkdir inference && cd inference
-# 下载超轻量级中文OCR模型的检测模型并解压
-wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar
-# 下载超轻量级中文OCR模型的识别模型并解压
-wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
-# 下载超轻量级中文OCR模型的文本方向分类器模型并解压
-wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar && tar xf ch_ppocr_mobile_v2.0_cls_infer.tar
-cd ..
+cd /path/to/ppocr_img
 ```
 
-解压完毕后应有如下文件结构：
+如果不使用提供的测试图片，可以将下方`--image_dir`参数替换为相应的测试图片路径
+<a name="211"></a>
+#### 2.1.1 中英文模型
+
+* 检测+方向分类器+识别全流程：设置方向分类器参数`--use_angle_cls true`后可对竖排文本进行识别。
+
+  ```bash
+  paddleocr --image_dir ./imgs/11.jpg --use_angle_cls true
+  ```
+
+  结果是一个list，每个item包含了文本框，文字和识别置信度
+
+  ```bash
+  [[[24.0, 36.0], [304.0, 34.0], [304.0, 72.0], [24.0, 74.0]], ['纯臻营养护发素', 0.964739]]
+  [[[24.0, 80.0], [172.0, 80.0], [172.0, 104.0], [24.0, 104.0]], ['产品信息/参数', 0.98069626]]
+  [[[24.0, 109.0], [333.0, 109.0], [333.0, 136.0], [24.0, 136.0]], ['（45元/每公斤，100公斤起订）', 0.9676722]]
+  ......
+  ```
+
+- 单独使用检测：设置`--rec`为`false`
+
+  ```bash
+  paddleocr --image_dir ./imgs/11.jpg --rec false
+  ```
+
+  结果是一个list，每个item只包含文本框
+
+  ```bash
+  [[26.0, 457.0], [137.0, 457.0], [137.0, 477.0], [26.0, 477.0]]
+  [[25.0, 425.0], [372.0, 425.0], [372.0, 448.0], [25.0, 448.0]]
+  [[128.0, 397.0], [273.0, 397.0], [273.0, 414.0], [128.0, 414.0]]
+  ......
+  ```
+
+- 单独使用识别：设置`--det`为`false`
+
+  ```bash
+  paddleocr --image_dir ./imgs_words/ch/word_1.jpg --det false
+  ```
+
+  结果是一个list，每个item只包含识别结果和识别置信度
+
+  ```bash
+  ['韩国小馆', 0.9907421]
+  ```
+
+
+更多whl包使用包括， whl包参数说明
+
+
+<a name="212"></a>
+#### 2.1.2 多语言模型
 
+Paddleocr目前支持80个语种，可以通过修改`--lang`参数进行切换，对于英文模型，指定`--lang=en`。
+
+``` bash
+paddleocr --image_dir ./imgs_en/254.jpg --lang=en
 ```
-├── ch_ppocr_mobile_v2.0_cls_infer
-│   ├── inference.pdiparams
-│   ├── inference.pdiparams.info
-│   └── inference.pdmodel
-├── ch_ppocr_mobile_v2.0_det_infer
-│   ├── inference.pdiparams
-│   ├── inference.pdiparams.info
-│   └── inference.pdmodel
-├── ch_ppocr_mobile_v2.0_rec_infer
-    ├── inference.pdiparams
-    ├── inference.pdiparams.info
-    └── inference.pdmodel
+
+<div align="center">
+    <img src="../imgs_en/254.jpg" width="300" height="600">
+    <img src="../imgs_results/multi_lang/img_02.jpg" width="600" height="600">
+</div>
+
+结果是一个list，每个item包含了文本框，文字和识别置信度
+
+```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]]]
+[('Montpelier Vermont', 0.97389287), [[69.0, 132.0], [501.0, 126.0], [501.0, 158.0], [70.0, 164.0]]]
+[('8022256183', 0.99810505), [[71.0, 175.0], [363.0, 170.0], [364.0, 202.0], [72.0, 207.0]]]
+[('REG 07-24-201706:59 PM', 0.93537045), [[73.0, 299.0], [653.0, 281.0], [654.0, 318.0], [74.0, 336.0]]]
+[('045555', 0.99346405), [[509.0, 331.0], [651.0, 325.0], [652.0, 356.0], [511.0, 362.0]]]
+[('CT1', 0.9988654), [[535.0, 367.0], [654.0, 367.0], [654.0, 406.0], [535.0, 406.0]]]
+......
 ```
 
-## 3.单张图像或者图像集合预测
+常用的多语言简写包括
 
-以下代码实现了文本检测、方向分类器和识别串联推理，在执行预测时，需要通过参数image_dir指定单张图像或者图像集合的路径、参数`det_model_dir`指定检测inference模型的路径、参数`rec_model_dir`指定识别inference模型的路径、参数`use_angle_cls`指定是否使用方向分类器、参数`cls_model_dir`指定方向分类器inference模型的路径、参数`use_space_char`指定是否预测空格字符。可视化识别结果默认保存到`./inference_results`文件夹里面。
+| 语种     | 缩写        |      | 语种     | 缩写   |      | 语种     | 缩写   |
+| -------- | ----------- | ---- | -------- | ------ | ---- | -------- | ------ |
+| 中文     | ch          |      | 法文     | fr     |      | 日文     | japan  |
+| 英文     | en          |      | 德文     | german |      | 韩文     | korean |
+| 繁体中文 | chinese_cht |      | 意大利文 | it     |      | 俄罗斯文 | ru     |
+
+全部语种及其对应的缩写列表可查看[多语言模型教程](./multi_languages.md)
+<a name="213"></a>
+#### 2.1.3 版面分析
+
+使用PaddleOCR的版面分析功能，需要指定`--type=structure`
 
 ```bash
+paddleocr --image_dir=./table/1.png --type=structure
+```
 
-# 预测image_dir指定的单张图像
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_mobile_v2.0_det_infer/"  --rec_model_dir="./inference/ch_ppocr_mobile_v2.0_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer/" --use_angle_cls=True --use_space_char=True
+- **返回结果说明**
 
-# 预测image_dir指定的图像集合
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/" --det_model_dir="./inference/ch_ppocr_mobile_v2.0_det_infer/"  --rec_model_dir="./inference/ch_ppocr_mobile_v2.0_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer/" --use_angle_cls=True --use_space_char=True
+  PP-Structure的返回结果为一个dict组成的list，示例如下
 
-# 如果想使用CPU进行预测，需设置use_gpu参数为False
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_mobile_v2.0_det_infer/"  --rec_model_dir="./inference/ch_ppocr_mobile_v2.0_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer/" --use_angle_cls=True --use_space_char=True --use_gpu=False
-```
+  ```shell
+  [{   'type': 'Text',
+        'bbox': [34, 432, 345, 462],
+        'res': ([[36.0, 437.0, 341.0, 437.0, 341.0, 446.0, 36.0, 447.0], [41.0, 454.0, 125.0, 453.0, 125.0, 459.0, 41.0, 460.0]],
+                  [('Tigure-6. The performance of CNN and IPT models using difforen', 0.90060663), ('Tent  ', 0.465441)])
+    }
+  ]
+  ```
+
+  其中各个字段说明如下
+
+  | 字段 | 说明                                                         |
+  | ---- | ------------------------------------------------------------ |
+  | type | 图片区域的类型                                               |
+  | bbox | 图片区域的在原图的坐标，分别[左上角x，左上角y，右下角x，右下角y] |
+  | res  | 图片区域的OCR或表格识别结果。<br>表格: 表格的HTML字符串; <br>OCR: 一个包含各个单行文字的检测坐标和识别结果的元组 |
+
+  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文件和图片名为表格在图片里的坐标。
+
+  ```
+  /output/table/1/
+    └─ res.txt										
+    └─ [454, 360, 824, 658].xlsx  表格识别结果
+    └─ [16, 2, 828, 305].jpg			被裁剪出的图片区域
+    └─ [17, 361, 404, 711].xlsx		表格识别结果
+  ```
+
+- **参数说明**
+
+  | 字段            | 说明                                     | 默认值                                       |
+  | --------------- | ---------------------------------------- | -------------------------------------------- |
+  | output          | excel和识别结果保存的地址                | ./output/table                               |
+  | table_max_len   | 表格结构模型预测时，图像的长边resize尺度 | 488                                          |
+  | table_model_dir | 表格结构模型 inference 模型地址          | None                                         |
+  | table_char_type | 表格结构模型所用字典地址                 | ../ppocr/utils/dict/table_structure_dict.txt |
 
-- 通用中文OCR模型
+  大部分参数和paddleocr whl包保持一致，见 [whl包文档](../doc/doc_ch/whl.md)
 
-请按照上述步骤下载相应的模型，并且更新相关的参数，示例如下：
+  
+<a name="22"></a>
+### 2.2 Python脚本使用
+<a name="221"></a>
+#### 2.2.1 中英文与多语言使用
+
+通过脚本使用PaddleOCR whl包。whl包会自动下载ppocr轻量级模型作为默认模型，
+
+* 检测+方向分类器+识别全流程
+
+```python
+from paddleocr import PaddleOCR, draw_ocr
+
+# Paddleocr目前支持的多语言语种可以通过修改lang参数进行切换
+# 例如`ch`, `en`, `fr`, `german`, `korean`, `japan`
+ocr = PaddleOCR(use_angle_cls=True, lang="ch")  # need to run only once to download and load model into memory
+img_path = './imgs/11.jpg'
+result = ocr.ocr(img_path, cls=True)
+for line in result:
+    print(line)
+
+# 显示结果
+from PIL import Image
+
+image = Image.open(img_path).convert('RGB')
+boxes = [line[0] for line in result]
+txts = [line[1][0] for line in result]
+scores = [line[1][1] for line in result]
+im_show = draw_ocr(image, boxes, txts, scores, font_path='./fonts/simfang.ttf')
+im_show = Image.fromarray(im_show)
+im_show.save('result.jpg')
+```
+
+结果是一个list，每个item包含了文本框，文字和识别置信度
 
 ```bash
-# 预测image_dir指定的单张图像
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_server_v2.0_det_infer/"  --rec_model_dir="./inference/ch_ppocr_server_v2.0_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer/" --use_angle_cls=True --use_space_char=True
+[[[24.0, 36.0], [304.0, 34.0], [304.0, 72.0], [24.0, 74.0]], ['纯臻营养护发素', 0.964739]]
+[[[24.0, 80.0], [172.0, 80.0], [172.0, 104.0], [24.0, 104.0]], ['产品信息/参数', 0.98069626]]
+[[[24.0, 109.0], [333.0, 109.0], [333.0, 136.0], [24.0, 136.0]], ['（45元/每公斤，100公斤起订）', 0.9676722]]
+......
 ```
 
-* 注意：
-    - 如果希望使用不支持空格的识别模型，在预测的时候需要注意：请将代码更新到最新版本，并添加参数 `--use_space_char=False`。
-    - 如果不希望使用方向分类器，在预测的时候需要注意：请将代码更新到最新版本，并添加参数 `--use_angle_cls=False`。
+结果可视化
+
+<div align="center">
+    <img src="../imgs_results/whl/11_det_rec.jpg" width="800">
+</div>
+<a name="222"></a>
+#### 2.2.2 版面分析使用
+
+```python
+import os
+import cv2
+from paddleocr import PPStructure,draw_structure_result,save_structure_res
 
+table_engine = PPStructure(show_log=True)
 
-更多的文本检测、识别串联推理使用方式请参考文档教程中[基于Python预测引擎推理](./inference.md)。
+save_folder = './output/table'
+img_path = './table/paper-image.jpg'
+img = cv2.imread(img_path)
+result = table_engine(img)
+save_structure_res(result, save_folder,os.path.basename(img_path).split('.')[0])
 
-此外，文档教程中也提供了中文OCR模型的其他预测部署方式：
-- [基于C++预测引擎推理](../../deploy/cpp_infer/readme.md)
-- [服务部署](../../deploy/hubserving)
-- [端侧部署（目前只支持静态图）](https://github.com/PaddlePaddle/PaddleOCR/tree/develop/deploy/lite)
+for line in result:
+    line.pop('img')
+    print(line)
+
+from PIL import Image
+
+font_path = './fonts/simfang.ttf' # PaddleOCR下提供字体包
+image = Image.open(img_path).convert('RGB')
+im_show = draw_structure_result(image, result,font_path=font_path)
+im_show = Image.fromarray(im_show)
+im_show.save('result.jpg')
+```

doc/doc_ch/whl.md

@@ -210,7 +210,7 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --use_angle_cls true
 ```bash
 [[[24.0, 36.0], [304.0, 34.0], [304.0, 72.0], [24.0, 74.0]], ['纯臻营养护发素', 0.964739]]
 [[[24.0, 80.0], [172.0, 80.0], [172.0, 104.0], [24.0, 104.0]], ['产品信息/参数', 0.98069626]]
-[[[24.0, 109.0], [333.0, 109.0], [333.0, 136.0], [24.0, 136.0]], ['（45元/每公斤，100公斤起订）', 0.9676722]]
+[[[24.0, 109.0], [333.0, 109.0], [333.0, 136.0], [24.0, 136.0]], ['（45元/每公斤，100公斤起订）', 0.9676722]]µ
 ......
 ```
 

doc/doc_en/multi_languages_en.md

@@ -198,13 +198,13 @@ If necessary, you can read related documents:
 
 | Language  | Abbreviation | | Language  | Abbreviation |
 | ---  | --- | --- | ---  | --- |
-|chinese and english|ch| |Arabic|ar|
-|english|en| |Hindi|hi|
-|french|fr| |Uyghur|ug|
-|german|german| |Persian|fa|
-|japan|japan| |Urdu|ur|
-|korean|korean| | Serbian(latin) |rs_latin|
-|chinese traditional |ch_tra| |Occitan |oc|
+|Chinese & English|ch| |Arabic|ar|
+|English|en| |Hindi|hi|
+|French|fr| |Uyghur|ug|
+|German|german| |Persian|fa|
+|Japan|japan| |Urdu|ur|
+|Korean|korean| | Serbian(latin) |rs_latin|
+|Chinese Traditional |chinese_cht| |Occitan |oc|
 | Italian |it| |Marathi|mr|
 |Spanish |es| |Nepali|ne|
 | Portuguese|pt| |Serbian(cyrillic)|rs_cyrillic|

doc/doc_en/quickstart_en.md

 
-# Quick start of Chinese OCR model
+# PaddleOCR Quick Start
 
-## 1. Prepare for the environment
+[PaddleOCR Quick Start](#paddleocr-quick-start)
 
-Please refer to [quick installation](./installation_en.md) to configure the PaddleOCR operating environment.
+* [1. Light Installation](#1-light-installation)
+  + [1.1 Install PaddlePaddle2.0](#11-install-paddlepaddle20)
+  + [1.2 Install PaddleOCR Whl Package](#12-install-paddleocr-whl-package)
+* [2. Easy-to-Use](#2-easy-to-use)
+  + [2.1 Use by command line](#21-use-by-command-line)
+    - [2.1.1 English and Chinese Model](#211-english-and-chinese-model)
+    - [2.1.2 Multi-language Model](#212-multi-language-model)
+    - [2.1.3 LayoutParser](#213-layoutparser)
+  + [2.2 Use by Code](#22-use-by-code)
+    - [2.2.1 Chinese & English Model and Multilingual Model](#221-chinese---english-model-and-multilingual-model)
+    - [2.2.2 LayoutParser](#222-layoutparser)
 
-* Note: Support the use of PaddleOCR through whl package installation，pelease refer  [PaddleOCR Package](./whl_en.md).
+<a name="1-light-installation"></a>
 
-## 2.inference models
+## 1. Light Installation
 
-The detection and recognition models on the mobile and server sides are as follows. For more models  (including multiple languages), please refer to [PP-OCR v2.0 series model list](../doc_ch/models_list.md)
+<a name="11-install-paddlepaddle20"></a>
 
-| Model introduction     | Model name      | Recommended scene          | Detection model | Direction Classifier | Recognition model |
-| ------------ | --------------- | ----------------|---- | ---------- | -------- |
-| Ultra-lightweight Chinese OCR model (8.1M) | ch_ppocr_mobile_v2.0_xx |Mobile-side/Server-side|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar)      |
-| Universal Chinese OCR model (143M)   | ch_ppocr_server_v2.0_xx |Server-side |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar)          |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar)    |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar)  |
+### 1.1 Install PaddlePaddle2.0
 
+```bash
+# If you have cuda9 or cuda10 installed on your machine, please run the following command to install
+python3 -m pip install paddlepaddle-gpu==2.0.0 -i https://mirror.baidu.com/pypi/simple
 
-* If `wget` is not installed in the windows environment, you can copy the link to the browser to download when downloading the model, then uncompress it and place it in the corresponding directory.
+# If you only have cpu on your machine, please run the following command to install
+python3 -m pip install paddlepaddle==2.0.0 -i https://mirror.baidu.com/pypi/simple
+```
 
-Copy the download address of the `inference model` for detection and recognition in the table above, and uncompress them.
+For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/install/quick) for operation.
 
-```
-mkdir inference && cd inference
-# Download the detection model and unzip
-wget {url/of/detection/inference_model} && tar xf {name/of/detection/inference_model/package}
-# Download the recognition model and unzip
-wget {url/of/recognition/inference_model} && tar xf {name/of/recognition/inference_model/package}
-# Download the direction classifier model and unzip
-wget {url/of/classification/inference_model} && tar xf {name/of/classification/inference_model/package}
-cd ..
-```
+<a name="12-install-paddleocr-whl-package"></a>
 
-Take the ultra-lightweight model as an example:
+### 1.2 Install PaddleOCR Whl Package
 
+```bash
+pip install "paddleocr>=2.0.1" # Recommend to use version 2.0.1+
 ```
-mkdir inference && cd inference
-# Download the detection model of the ultra-lightweight Chinese OCR model and uncompress it
-wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar
-# Download the recognition model of the ultra-lightweight Chinese OCR model and uncompress it
-wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
-# Download the angle classifier model of the ultra-lightweight Chinese OCR model and uncompress it
-wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar && tar xf ch_ppocr_mobile_v2.0_cls_infer.tar
-cd ..
+
+- **For windows users:** 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 [here](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)
+
+- **For layout analysis users**, run the following command to install **Layout-Parser**
+
+  ```bash
+  pip3 install -U https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
+  ```
+
+<a name="2-easy-to-use"></a>
+
+## 2. Easy-to-Use
+
+<a name="21-use-by-command-line"></a>
+
+### 2.1 Use by command line
+
+PaddleOCR provides a series of test images, click xx to download, and then switch to the corresponding directory in the terminal
+
+```bash
+cd /path/to/ppocr_img
 ```
 
-After decompression, the file structure should be as follows:
+If you do not use the provided test image, you can replace the following `--image_dir` parameter with the corresponding test image path
+
+<a name="211-english-and-chinese-model"></a>
+
+#### 2.1.1 Chinese and English Model
+
+* Detection, direction classification and recognition: set the direction classifier parameter`--use_angle_cls true` to recognize vertical text.
+
+  ```bash
+  paddleocr --image_dir ./imgs_en/img_12.jpg --use_angle_cls true --lang en
+  ```
+
+  Output will be a list, each item contains bounding box, text and recognition confidence
+
+  ```bash
+  [[[442.0, 173.0], [1169.0, 173.0], [1169.0, 225.0], [442.0, 225.0]], ['ACKNOWLEDGEMENTS', 0.99283075]]
+  [[[393.0, 340.0], [1207.0, 342.0], [1207.0, 389.0], [393.0, 387.0]], ['We would like to thank all the designers and', 0.9357758]]
+  [[[399.0, 398.0], [1204.0, 398.0], [1204.0, 433.0], [399.0, 433.0]], ['contributors whohave been involved in the', 0.9592447]]
+  ......
+  ```
+
+* Only detection: set `--rec` to `false`
+
+  ```bash
+  paddleocr --image_dir ./imgs_en/img_12.jpg --rec false
+  ```
+
+  Output will be a list, each item only contains bounding box
+
+  ```bash
+  [[756.0, 812.0], [805.0, 812.0], [805.0, 830.0], [756.0, 830.0]]
+  [[820.0, 803.0], [1085.0, 801.0], [1085.0, 836.0], [820.0, 838.0]]
+  [[393.0, 801.0], [715.0, 805.0], [715.0, 839.0], [393.0, 836.0]]
+  ......
+  ```
+
+* Only recognition: set `--det` to `false`
+
+  ```bash
+  paddleocr --image_dir ./imgs_words_en/word_10.png --det false --lang en
+  ```
 
+  Output will be a list, each item contains text and recognition confidence
+
+  ```bash
+  ['PAIN', 0.990372]
+  ```
+
+More whl package usage can be found in [whl package](./whl_en.md)
+<a name="212-multi-language-model"></a>
+
+#### 2.1.2 Multi-language Model
+
+Paddleocr currently supports 80 languages, which can be switched by modifying the `--lang` parameter.
+
+``` bash
+paddleocr --image_dir ./doc/imgs_en/254.jpg --lang=en
 ```
-├── ch_ppocr_mobile_v2.0_cls_infer
-│   ├── inference.pdiparams
-│   ├── inference.pdiparams.info
-│   └── inference.pdmodel
-├── ch_ppocr_mobile_v2.0_det_infer
-│   ├── inference.pdiparams
-│   ├── inference.pdiparams.info
-│   └── inference.pdmodel
-├── ch_ppocr_mobile_v2.0_rec_infer
-    ├── inference.pdiparams
-    ├── inference.pdiparams.info
-    └── inference.pdmodel
+
+<div align="center">
+    <img src="../imgs_en/254.jpg" width="300" height="600">
+    <img src="../imgs_results/multi_lang/img_02.jpg" width="600" height="600">
+</div>
+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]]]
+[('Montpelier Vermont', 0.97389287), [[69.0, 132.0], [501.0, 126.0], [501.0, 158.0], [70.0, 164.0]]]
+[('8022256183', 0.99810505), [[71.0, 175.0], [363.0, 170.0], [364.0, 202.0], [72.0, 207.0]]]
+[('REG 07-24-201706:59 PM', 0.93537045), [[73.0, 299.0], [653.0, 281.0], [654.0, 318.0], [74.0, 336.0]]]
+[('045555', 0.99346405), [[509.0, 331.0], [651.0, 325.0], [652.0, 356.0], [511.0, 362.0]]]
+[('CT1', 0.9988654), [[535.0, 367.0], [654.0, 367.0], [654.0, 406.0], [535.0, 406.0]]]
+......
 ```
 
-## 3. Single image or image set prediction
+Commonly used multilingual abbreviations include
 
-* The following code implements text detection、angle class and recognition process. When performing prediction, you need to specify the path of a single image or image set through the parameter `image_dir`, the parameter `det_model_dir` specifies the path to detect the inference model, the parameter `rec_model_dir` specifies the path to identify the inference model, the parameter `use_angle_cls` specifies whether to use the direction classifier, the parameter `cls_model_dir` specifies the path to identify the direction classifier model, the parameter `use_space_char` specifies whether to predict the space char. The visual results are saved to the `./inference_results` folder by default.
+| Language            | Abbreviation |      | Language | Abbreviation |      | Language | Abbreviation |
+| ------------------- | ------------ | ---- | -------- | ------------ | ---- | -------- | ------------ |
+| Chinese & English   | ch           |      | French   | fr           |      | Japanese | japan        |
+| English             | en           |      | German   | german       |      | Korean   | korean       |
+| Chinese Traditional | chinese_cht  |      | Italian  | it           |      | Russian  | ru           |
 
+A list of all languages and their corresponding abbreviations can be found in [Multi-Language Model Tutorial](./multi_languages_en.md)
+<a name="213-layoutparser"></a>
 
+#### 2.1.3 LayoutParser
+
+To use the layout analysis function of PaddleOCR, you need to specify `--type=structure`
 
 ```bash
+paddleocr --image_dir=../doc/table/1.png --type=structure
+```
 
-# Predict a single image specified by image_dir
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_mobile_v2.0_det_infer/"  --rec_model_dir="./inference/ch_ppocr_mobile_v2.0_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer/" --use_angle_cls=True --use_space_char=True
+- **Results Format**
 
-# Predict imageset specified by image_dir
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/" --det_model_dir="./inference/ch_ppocr_mobile_v2.0_det_infer/"  --rec_model_dir="./inference/ch_ppocr_mobile_v2.0_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer/" --use_angle_cls=True --use_space_char=True
+  The returned results of PP-Structure is a list composed of a dict, an example is as follows
 
-# If you want to use the CPU for prediction, you need to set the use_gpu parameter to False
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_mobile_v2.0_det_infer/"  --rec_model_dir="./inference/ch_ppocr_mobile_v2.0_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer/" --use_angle_cls=True --use_space_char=True --use_gpu=False
-```
+  ```shell
+  [
+    {   'type': 'Text',
+        'bbox': [34, 432, 345, 462],
+        'res': ([[36.0, 437.0, 341.0, 437.0, 341.0, 446.0, 36.0, 447.0], [41.0, 454.0, 125.0, 453.0, 125.0, 459.0, 41.0, 460.0]],
+                  [('Tigure-6. The performance of CNN and IPT models using difforen', 0.90060663), ('Tent  ', 0.465441)])
+    }
+  ]
+  ```
+
+  The description of each field in dict is as follows
+
+  | Parameter | Description                                                  |
+  | --------- | ------------------------------------------------------------ |
+  | type      | Type of image area                                           |
+  | bbox      | The coordinates of the image area in the original image, respectively [left upper x, left upper y, right bottom x, right bottom y] |
+  | res       | OCR or table recognition result of image area。<br> Table: HTML string of the table; <br> OCR: A tuple containing the detection coordinates and recognition results of each single line of text |
+
+- **Parameter Description：**
+
+  | Parameter       | Description                                                  | Default value                                |
+  | --------------- | ------------------------------------------------------------ | -------------------------------------------- |
+  | output          | The path where excel and recognition results are saved       | ./output/table                               |
+  | table_max_len   | The long side of the image is resized in table structure model | 488                                          |
+  | table_model_dir | inference model path of table structure model                | None                                         |
+  | table_char_type | dict path of table structure model                           | ../ppocr/utils/dict/table_structure_dict.txt |
 
-- Universal Chinese OCR model
+<a name="22-use-by-code"></a>
 
-Please follow the above steps to download the corresponding models and update the relevant parameters, The example is as follows.
+### 2.2 Use by Code
+<a name="221-chinese---english-model-and-multilingual-model"></a>
 
+#### 2.2.1 Chinese & English Model and Multilingual Model
+
+* detection, angle classification and recognition:
+
+```python
+from paddleocr import PaddleOCR,draw_ocr
+# Paddleocr supports Chinese, English, French, German, Korean and Japanese.
+# You can set the parameter `lang` as `ch`, `en`, `fr`, `german`, `korean`, `japan`
+# to switch the language model in order.
+ocr = PaddleOCR(use_angle_cls=True, lang='en') # need to run only once to download and load model into memory
+img_path = './imgs_en/img_12.jpg'
+result = ocr.ocr(img_path, cls=True)
+for line in result:
+    print(line)
+
+
+# draw result
+from PIL import Image
+image = Image.open(img_path).convert('RGB')
+boxes = [line[0] for line in result]
+txts = [line[1][0] for line in result]
+scores = [line[1][1] for line in result]
+im_show = draw_ocr(image, boxes, txts, scores, font_path='./fonts/simfang.ttf')
+im_show = Image.fromarray(im_show)
+im_show.save('result.jpg')
 ```
-# Predict a single image specified by image_dir
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_server_v2.0_det_infer/"  --rec_model_dir="./inference/ch_ppocr_server_v2.0_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer/" --use_angle_cls=True --use_space_char=True
+
+Output will be a list, each item contains bounding box, text and recognition confidence
+
+```bash
+[[[442.0, 173.0], [1169.0, 173.0], [1169.0, 225.0], [442.0, 225.0]], ['ACKNOWLEDGEMENTS', 0.99283075]]
+[[[393.0, 340.0], [1207.0, 342.0], [1207.0, 389.0], [393.0, 387.0]], ['We would like to thank all the designers and', 0.9357758]]
+[[[399.0, 398.0], [1204.0, 398.0], [1204.0, 433.0], [399.0, 433.0]], ['contributors whohave been involved in the', 0.9592447]]
+......
 ```
 
-* Note
-    - If you want to use the recognition model which does not support space char recognition, please update the source code to the latest version and add parameters `--use_space_char=False`.
-    - If you do not want to use direction classifier, please update the source code to the latest version and add parameters `--use_angle_cls=False`.
+Visualization of results
 
+<div align="center">
+    <img src="../imgs_results/whl/12_det_rec.jpg" width="800">
+</div>
+<a name="222-layoutparser"></a>
 
-For more text detection and recognition tandem reasoning, please refer to the document tutorial
-: [Inference with Python inference engine](./inference_en.md)。
+#### 2.2.2 LayoutParser
+
+```python
+import os
+import cv2
+from paddleocr import PPStructure,draw_structure_result,save_structure_res
+
+table_engine = PPStructure(show_log=True)
+
+save_folder = './output/table'
+img_path = './table/1.png'
+img = cv2.imread(img_path)
+result = table_engine(img)
+save_structure_res(result, save_folder,os.path.basename(img_path).split('.')[0])
+
+for line in result:
+    line.pop('img')
+    print(line)
+
+from PIL import Image
+
+font_path = './fonts/simfang.ttf'
+image = Image.open(img_path).convert('RGB')
+im_show = draw_structure_result(image, result,font_path=font_path)
+im_show = Image.fromarray(im_show)
+im_show.save('result.jpg')
+```
 
-In addition, the tutorial also provides other deployment methods for the Chinese OCR model:
-- [Server-side C++ inference](../../deploy/cpp_infer/readme_en.md)
-- [Service deployment](../../deploy/hubserving)
-- [End-to-end deployment](https://github.com/PaddlePaddle/PaddleOCR/tree/develop/deploy/lite)

doc/doc_en/whl_en.md

-# paddleocr package
+# Paddleocr Package
 
 ## 1 Get started quickly
 ### 1.1 install package