Merge pull request #3896 from Evezerest/dygraph

Upgrade document structure

Merge pull request #3896 from Evezerest/dygraph
Upgrade document structure
c1ed243f · DanielYang · GitHub · 60e9d6c6 · 6c86bcb3 · c1ed243f
30 changed file
--- a/README.md
+++ b/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)
+- [PaddleOCR Overview and Installation](./doc/doc_en/paddleOCR_overview_en.md)
- Algorithm Introduction
+- PP-OCR Industry Landing: from Training to Deployment
-    - [Text Detection Algorithm](./doc/doc_en/algorithm_overview_en.md)
+    - [PP-OCR Model and Configuration](./doc/doc_en/models_and_config_en.md)
-    - [Text Recognition Algorithm](./doc/doc_en/algorithm_overview_en.md)
+        - [PP-OCR Model Download](./doc/doc_en/models_list_en.md)
-    - [PP-OCR Pipeline](#PP-OCR-Pipeline)
+        - [Yml Configuration](./doc/doc_en/config_en.md)
- Model Training/Evaluation
+        - [Python Inference](./doc/doc_en/inference_en.md)
-    - [Text Detection](./doc/doc_en/detection_en.md)
+    - [PP-OCR Training](./doc/doc_en/training_en.md)
-    - [Text Recognition](./doc/doc_en/recognition_en.md)
+        - [Text Detection](./doc/doc_en/detection_en.md)
-    - [Direction Classification](./doc/doc_en/angle_class_en.md)
+        - [Text Recognition](./doc/doc_en/recognition_en.md)
-    - [Yml Configuration](./doc/doc_en/config_en.md)
+        - [Direction Classification](./doc/doc_en/angle_class_en.md)
- Inference and Deployment
+    - Inference and Deployment
-    - [Quick Inference Based on PIP](./doc/doc_en/whl_en.md)
+        - [Python Inference](./doc/doc_en/inference_en.md)
-    - [Python Inference](./doc/doc_en/inference_en.md)
+        - [C++ Inference](./deploy/cpp_infer/readme_en.md)
-    - [C++ Inference](./deploy/cpp_infer/readme_en.md)
+        - [Serving](./deploy/pdserving/README.md)
-    - [Serving](./deploy/pdserving/README.md)
+        - [Mobile](./deploy/lite/readme_en.md)
-    - [Mobile](./deploy/lite/readme_en.md)
+        - [Benchmark](./doc/doc_en/benchmark_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)

--- a/README_ch.md
+++ b/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)
+- [快速开始](./doc/doc_ch/quickstart.md)
- [中文OCR模型快速使用](./doc/doc_ch/quickstart.md)
+- [PaddleOCR全景图与安装](./doc/doc_ch/paddleOCR_overview.md)
- [多语言OCR模型快速使用](./doc/doc_ch/multi_languages.md)
+- PP-OCR产业落地：从训练到部署
- [代码组织结构](./doc/doc_ch/tree.md)
+    - [PP-OCR模型与配置文件](./doc/doc_ch/models_and_config.md)
- 算法介绍
+        - [PP-OCR模型下载](./doc/doc_ch/models_list.md)
-    - [文本检测](./doc/doc_ch/algorithm_overview.md)
+        - [配置文件内容与生成](./doc/doc_ch/config.md)
-    - [文本识别](./doc/doc_ch/algorithm_overview.md)
+        - [模型库快速使用](./doc/doc_ch/inference.md)
-    - [PP-OCR Pipeline](#PP-OCR)
+    - [PP-OCR模型训练](./doc/doc_ch/training.md)
-    - [端到端PGNet算法](./doc/doc_ch/pgnet.md)
+        - [文本检测](./doc/doc_ch/detection.md)
- 模型训练/评估
+        - [文本识别](./doc/doc_ch/recognition.md)
-    - [文本检测](./doc/doc_ch/detection.md)
+        - [方向分类器](./doc/doc_ch/angle_class.md)
-    - [文本识别](./doc/doc_ch/recognition.md)
+    - PP-OCR模型推理部署
-    - [方向分类器](./doc/doc_ch/angle_class.md)
+        - [基于Python脚本预测引擎推理](./doc/doc_ch/inference.md)
-    - [yml参数配置文件介绍](./doc/doc_ch/config.md)
+        - [基于C++预测引擎推理](./deploy/cpp_infer/readme.md)
- 预测部署
+        - [服务化部署](./deploy/pdserving/README_CN.md)
-    - [基于pip安装whl包快速推理](./doc/doc_ch/whl.md)
+        - [端侧部署](./deploy/lite/readme.md)
-    - [基于Python脚本预测引擎推理](./doc/doc_ch/inference.md)
+        - [Benchmark](./doc/doc_ch/benchmark.md)
-    - [基于C++预测引擎推理](./deploy/cpp_infer/readme.md)
+- [PP-Structure信息提取](./ppstructure/README_ch.md)
-    - [服务化部署](./deploy/pdserving/README_CN.md)
+    - [版面分析](./ppstructure/layout/README_ch.md)
-    - [端侧部署](./deploy/lite/readme.md)
+    - [表格识别](./ppstructure/table/README_ch.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)
 - 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>

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

--- a/doc/doc_ch/paddleOCR_overview.md
+++ b/doc/doc_ch/paddleOCR_overview.md
+# PaddleOCR全景图与项目克隆
--- a/doc/doc_ch/quickstart.md
+++ b/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 |
+```bash
-| ------------ | --------------- | ----------------|---- | ---------- | -------- |
+python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
-| 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)  |
+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
+cd /path/to/ppocr_img
-# 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 ..
 ```
-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
+<div align="center">
-│   ├── inference.pdiparams.info
+    <img src="../imgs_en/254.jpg" width="300" height="600">
-│   └── inference.pdmodel
+    <img src="../imgs_results/multi_lang/img_02.jpg" width="600" height="600">
-├── ch_ppocr_mobile_v2.0_det_infer
+</div>
-│   ├── inference.pdiparams
-│   ├── inference.pdiparams.info
+结果是一个list，每个item包含了文本框，文字和识别置信度
-│   └── inference.pdmodel
-├── ch_ppocr_mobile_v2.0_rec_infer
+```text
-    ├── inference.pdiparams
+[('PHO CAPITAL', 0.95723116), [[66.0, 50.0], [327.0, 44.0], [327.0, 76.0], [67.0, 82.0]]]
-    ├── inference.pdiparams.info
+[('107 State Street', 0.96311164), [[72.0, 90.0], [451.0, 84.0], [452.0, 116.0], [73.0, 121.0]]]
-    └── inference.pdmodel
+[('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
+- **返回结果说明**
-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
-# 预测image_dir指定的图像集合
+  PP-Structure的返回结果为一个dict组成的list，示例如下
-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
-# 如果想使用CPU进行预测，需设置use_gpu参数为False
+  ```shell
-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
+  [{   '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
+[[[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]]
-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, 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：
+结果可视化
-    - 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`。
+<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：
+for line in result:
- [基于C++预测引擎推理](../../deploy/cpp_infer/readme.md)
+    line.pop('img')
- [服务部署](../../deploy/hubserving)
+    print(line)
- [端侧部署（目前只支持静态图）](https://github.com/PaddlePaddle/PaddleOCR/tree/develop/deploy/lite)
+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')
+```
--- a/doc/doc_ch/training.md
+++ b/doc/doc_ch/training.md
--- a/doc/doc_ch/whl.md
+++ b/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
 ......
 ```

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

--- a/doc/doc_en/paddleOCR_overview_en.md
+++ b/doc/doc_en/paddleOCR_overview_en.md
--- a/doc/doc_en/quickstart_en.md
+++ b/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 |
+### 1.1 Install PaddlePaddle2.0
-| ------------ | --------------- | ----------------|---- | ---------- | -------- |
-| 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)  |
+```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.
-```
+<a name="12-install-paddleocr-whl-package"></a>
-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 ..
-```
-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
+- **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).
-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
+  Reference: [Solve shapely installation on windows](https://stackoverflow.com/questions/44398265/install-shapely-oserror-winerror-126-the-specified-module-could-not-be-found)
-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
+- **For layout analysis users**, run the following command to install **Layout-Parser**
-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 ..
+  ```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
+<div align="center">
-│   ├── inference.pdiparams.info
+    <img src="../imgs_en/254.jpg" width="300" height="600">
-│   └── inference.pdmodel
+    <img src="../imgs_results/multi_lang/img_02.jpg" width="600" height="600">
-├── ch_ppocr_mobile_v2.0_det_infer
+</div>
-│   ├── inference.pdiparams
+The result is a list, each item contains a text box, text and recognition confidence
-│   ├── inference.pdiparams.info
-│   └── inference.pdmodel
+```text
-├── ch_ppocr_mobile_v2.0_rec_infer
+[('PHO CAPITAL', 0.95723116), [[66.0, 50.0], [327.0, 44.0], [327.0, 76.0], [67.0, 82.0]]]
-    ├── inference.pdiparams
+[('107 State Street', 0.96311164), [[72.0, 90.0], [451.0, 84.0], [452.0, 116.0], [73.0, 121.0]]]
-    ├── inference.pdiparams.info
+[('Montpelier Vermont', 0.97389287), [[69.0, 132.0], [501.0, 126.0], [501.0, 158.0], [70.0, 164.0]]]
-    └── inference.pdmodel
+[('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
+- **Results Format**
-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
-# Predict imageset specified by image_dir
+  The returned results of PP-Structure is a list composed of a dict, an example is as follows
-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
-# If you want to use the CPU for prediction, you need to set the use_gpu parameter to False
+  ```shell
-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
+  [
-```
+    {   '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
+Visualization of results
-    - 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`.
+<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
+#### 2.2.2 LayoutParser
-: [Inference with Python inference engine](./inference_en.md)。
+```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)
--- a/doc/doc_en/training_en.md
+++ b/doc/doc_en/training_en.md
--- a/doc/doc_en/whl_en.md
+++ b/doc/doc_en/whl_en.md
-# paddleocr package
+# Paddleocr Package
 ## 1 Get started quickly
 ### 1.1 install package

--- a/doc/install/linux/anaconda_download.png
+++ b/doc/install/linux/anaconda_download.png
--- a/doc/install/linux/conda_create.png
+++ b/doc/install/linux/conda_create.png
--- a/doc/install/mac/anaconda_start.png
+++ b/doc/install/mac/anaconda_start.png
--- a/doc/install/mac/conda_activate.png
+++ b/doc/install/mac/conda_activate.png
--- a/doc/install/mac/conda_create.png
+++ b/doc/install/mac/conda_create.png
--- a/doc/install/windows/Anaconda_download.png
+++ b/doc/install/windows/Anaconda_download.png
--- a/doc/install/windows/anaconda_install_env.png
+++ b/doc/install/windows/anaconda_install_env.png
--- a/doc/install/windows/anaconda_install_folder.png
+++ b/doc/install/windows/anaconda_install_folder.png
--- a/doc/install/windows/anaconda_prompt.png
+++ b/doc/install/windows/anaconda_prompt.png
--- a/doc/install/windows/conda_list_env.png
+++ b/doc/install/windows/conda_list_env.png
--- a/doc/install/windows/conda_new_env.png
+++ b/doc/install/windows/conda_new_env.png
--- a/ppstructure/README.md
+++ b/ppstructure/README.md
@@ -124,8 +124,6 @@ Most of the parameters are consistent with the paddleocr whl package, see [doc o
 After running, each image will have a directory with the same name under the directory specified in the output field. Each table in the picture will be stored as an excel and figure area will be cropped and saved, the excel and image file name will be the coordinates of the table in the image.
 ## 4. PP-Structure Pipeline
-the process is as follows
 ![pipeline](../doc/table/pipeline_en.jpg)
 In PP-Structure, the image will be analyzed by layoutparser first. In the layout analysis, the area in the image will be classified, including **text, title, image, list and table** 5 categories. For the first 4 types of areas, directly use the PP-OCR to complete the text detection and recognition. The table area will  be converted to an excel file of the same table style via Table OCR.