diff --git a/README.md b/README.md
index 95f35277a1d634c87d5720c7151d066b09dbdae7..99d20357b427477c13a560b54849dcbb831d0ee0 100644
--- a/README.md
+++ b/README.md
@@ -112,6 +112,10 @@ For a new language request, please refer to [Guideline for new language_requests
- [Text Recognition](./doc/doc_en/recognition_en.md)
- [Text Direction Classification](./doc/doc_en/angle_class_en.md)
- [Yml Configuration](./doc/doc_en/config_en.md)
+ - PP-OCR Models Compression
+ - [Knowledge Distillation](./doc/doc_en/knowledge_distillation_en.md)
+ - [Model Quantization](./deploy/slim/quantization/README_en.md)
+ - [Model Pruning](./deploy/slim/prune/README_en.md)
- Inference and Deployment
- [C++ Inference](./deploy/cpp_infer/readme_en.md)
- [Serving](./deploy/pdserving/README.md)
diff --git a/README_ch.md b/README_ch.md
index 3788f9f0d4003a0a8aa636cd1dd6148936598411..6eb57fecebb6e9b22dd09f730c84f9f15ac8dc9c 100755
--- a/README_ch.md
+++ b/README_ch.md
@@ -79,22 +79,26 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
## 文档教程
- [运行环境准备](./doc/doc_ch/environment.md)
-- [快速开始（中英文/多语言/文档分析）](./doc/doc_ch/quickstart.md)
+- [快速开始（中英文/多语言/版面分析）](./doc/doc_ch/quickstart.md)
- [PaddleOCR全景图与项目克隆](./doc/doc_ch/paddleOCR_overview.md)
- PP-OCR产业落地：从训练到部署
- [PP-OCR模型库](./doc/doc_ch/models.md)
- [PP-OCR模型下载](./doc/doc_ch/models_list.md)
- - [PP-OCR模型库快速推理](./doc/doc_ch/inference_ppocr.md)
+ - [Python引擎的PP-OCR模型库推理](./doc/doc_ch/inference_ppocr.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)
- [文本方向分类器](./doc/doc_ch/angle_class.md)
- - [知识蒸馏](./doc/doc_ch/knowledge_distillation.md)
- [配置文件内容与生成](./doc/doc_ch/config.md)
+ - PP-OCR模型压缩
+ - [知识蒸馏](./doc/doc_ch/knowledge_distillation.md)
+ - [模型量化](./deploy/slim/quantization/README.md)
+ - [模型裁剪](./deploy/slim/prune/README.md)
- PP-OCR模型推理部署
- [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)
+ - [Paddle2ONNX模型转化与预测](./deploy/paddle2onnx/readme.md)
- [Benchmark](./doc/doc_ch/benchmark.md)
- [PP-Structure信息提取](./ppstructure/README_ch.md)
- [版面分析](./ppstructure/layout/README_ch.md)
diff --git a/deploy/paddle2onnx/readme.md b/deploy/paddle2onnx/readme.md
index e08f2adee5d315cecba703ecdf515c09cd1569d2..8e821892142d65caddd6fa3bd8ff24a372fe9a5d 100644
--- a/deploy/paddle2onnx/readme.md
+++ b/deploy/paddle2onnx/readme.md
@@ -1,4 +1,4 @@
-# paddle2onnx 模型转化与预测
+# Paddle2ONNX模型转化与预测
本章节介绍 PaddleOCR 模型如何转化为 ONNX 模型，并基于 ONNXRuntime 引擎预测。
diff --git a/deploy/pdserving/README_CN.md b/deploy/pdserving/README_CN.md
index ee83b73b851d6188072bdb79d6130a809c3823e0..afd355bac098a3c13c36476e2967d8f94e8cd306 100644
--- a/deploy/pdserving/README_CN.md
+++ b/deploy/pdserving/README_CN.md
@@ -8,8 +8,7 @@ PaddleOCR提供2种服务部署方式：
# 基于PaddleServing的服务部署
-本文档将介绍如何使用[PaddleServing](https://github.com/PaddlePaddle/Serving/blob/develop/README_CN.md)工具部署PPOCR
-动态图模型的pipeline在线服务。
+本文档将介绍如何使用[PaddleServing](https://github.com/PaddlePaddle/Serving/blob/develop/README_CN.md)工具部署PP-OCR动态图模型的pipeline在线服务。
相比较于hubserving部署，PaddleServing具备以下优点：
- 支持客户端和服务端之间高并发和高效通信
@@ -59,7 +58,7 @@ pip3 install paddle_serving_app-0.7.0-py3-none-any.whl
使用PaddleServing做服务化部署时，需要将保存的inference模型转换为serving易于部署的模型。
-首先，下载PPOCR的[inference模型](https://github.com/PaddlePaddle/PaddleOCR#pp-ocr-series-model-listupdate-on-september-8th)
+首先，下载PP-OCR的[inference模型](https://github.com/PaddlePaddle/PaddleOCR#pp-ocr-series-model-listupdate-on-september-8th)
```bash
# 下载并解压 OCR 文本检测模型
@@ -107,7 +106,7 @@ python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_rec_infer/ \
1. 下载PaddleOCR代码，若已下载可跳过此步骤
```
git clone https://github.com/PaddlePaddle/PaddleOCR
-
+
# 进入到工作目录
cd PaddleOCR/deploy/pdserving/
```
diff --git a/deploy/slim/prune/README.md b/deploy/slim/prune/README.md
index c438572318f57fdfe9066ff2135156d7129bee4c..6d04f1648705071d70c1e9f17cd30d6825f92467 100644
--- a/deploy/slim/prune/README.md
+++ b/deploy/slim/prune/README.md
@@ -1,5 +1,5 @@
-## 介绍
+# PP-OCR模型裁剪
复杂的模型有利于提高模型的性能，但也导致模型中存在一定冗余，模型裁剪通过移出网络模型中的子模型来减少这种冗余，达到减少模型计算复杂度，提高模型推理性能的目的。
本教程将介绍如何使用飞桨模型压缩库PaddleSlim做PaddleOCR模型的压缩。
@@ -7,13 +7,13 @@
在开始本教程之前，建议先了解：
-1. [PaddleOCR模型的训练方法](../../../doc/doc_ch/quickstart.md)
+1. [PaddleOCR模型的训练方法](../../../doc/doc_ch/training.md)
2. [模型裁剪教程](https://github.com/PaddlePaddle/PaddleSlim/blob/release%2F2.0.0/docs/zh_cn/tutorials/pruning/dygraph/filter_pruning.md)
-
## 快速开始
模型裁剪主要包括四个步骤：
+
1. 安装 PaddleSlim
2. 准备训练好的模型
3. 敏感度分析、裁剪训练
@@ -35,17 +35,20 @@ python3 setup.py install
加载预训练模型后，通过对现有模型的每个网络层进行敏感度分析，得到敏感度文件：sen.pickle，可以通过PaddleSlim提供的[接口](https://github.com/PaddlePaddle/PaddleSlim/blob/9b01b195f0c4bc34a1ab434751cb260e13d64d9e/paddleslim/dygraph/prune/filter_pruner.py#L75)加载文件，获得各网络层在不同裁剪比例下的精度损失。从而了解各网络层冗余度，决定每个网络层的裁剪比例。
敏感度文件内容格式：
- sen.pickle(Dict){
+```
+sen.pickle(Dict){
'layer_weight_name_0': sens_of_each_ratio(Dict){'pruning_ratio_0': acc_loss, 'pruning_ratio_1': acc_loss}
'layer_weight_name_1': sens_of_each_ratio(Dict){'pruning_ratio_0': acc_loss, 'pruning_ratio_1': acc_loss}
}
- 例子：
+例子：
{
'conv10_expand_weights': {0.1: 0.006509952684312718, 0.2: 0.01827734339798862, 0.3: 0.014528405644659832, 0.6: 0.06536008804270439, 0.8: 0.11798612250664964, 0.7: 0.12391408417493704, 0.4: 0.030615754498018757, 0.5: 0.047105205602406594}
'conv10_linear_weights': {0.1: 0.05113190831455035, 0.2: 0.07705573833558801, 0.3: 0.12096721757739311, 0.6: 0.5135061352930738, 0.8: 0.7908166677143281, 0.7: 0.7272187676899062, 0.4: 0.1819252083008504, 0.5: 0.3728054727792405}
}
-加载敏感度文件后会返回一个字典，字典中的keys为网络模型参数模型的名字，values为一个字典，里面保存了相应网络层的裁剪敏感度信息。例如在例子中，conv10_expand_weights所对应的网络层在裁掉10%的卷积核后模型性能相较原模型会下降0.65%，详细信息可见[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim/blob/release/2.0-alpha/docs/zh_cn/algo/algo.md)
+```
+
+加载敏感度文件后会返回一个字典，字典中的keys为网络模型参数模型的名字，values为一个字典，里面保存了相应网络层的裁剪敏感度信息。例如在例子中，conv10_expand_weights所对应的网络层在裁掉10%的卷积核后模型性能相较原模型会下降0.65%，详细信息可见[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/algo/algo.md#2-%E5%8D%B7%E7%A7%AF%E6%A0%B8%E5%89%AA%E8%A3%81%E5%8E%9F%E7%90%86)
进入PaddleOCR根目录，通过以下命令对模型进行敏感度分析训练：
```bash
diff --git a/deploy/slim/prune/README_en.md b/deploy/slim/prune/README_en.md
index f8fbed47ca1c788ea816cc76f1092b17f0ea5219..ceb9e204a74b5460d74766e8df154d22f39bd843 100644
--- a/deploy/slim/prune/README_en.md
+++ b/deploy/slim/prune/README_en.md
@@ -1,5 +1,5 @@
-## Introduction
+# PP-OCR Models Pruning
Generally, a more complex model would achive better performance in the task, but it also leads to some redundancy in the model. Model Pruning is a technique that reduces this redundancy by removing the sub-models in the neural network model, so as to reduce model calculation complexity and improve model inference performance.
@@ -37,25 +37,27 @@ PaddleOCR also provides a series of [models](../../../doc/doc_en/models_list_en.
After the pre-trained model is loaded, sensitivity analysis is performed on each network layer of the model to understand the redundancy of each network layer, and save a sensitivity file which named: sen.pickle. After that, user could load the sensitivity file via the [methods provided by PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/prune/sensitive.py#L221) and determining the pruning ratio of each network layer automatically. For specific details of sensitivity analysis, see：[Sensitivity analysis](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/en/tutorials/image_classification_sensitivity_analysis_tutorial_en.md)
The data format of sensitivity file：
- sen.pickle(Dict){
+
+```
+sen.pickle(Dict){
'layer_weight_name_0': sens_of_each_ratio(Dict){'pruning_ratio_0': acc_loss, 'pruning_ratio_1': acc_loss}
'layer_weight_name_1': sens_of_each_ratio(Dict){'pruning_ratio_0': acc_loss, 'pruning_ratio_1': acc_loss}
}
-
- example：
+example：
{
'conv10_expand_weights': {0.1: 0.006509952684312718, 0.2: 0.01827734339798862, 0.3: 0.014528405644659832, 0.6: 0.06536008804270439, 0.8: 0.11798612250664964, 0.7: 0.12391408417493704, 0.4: 0.030615754498018757, 0.5: 0.047105205602406594}
'conv10_linear_weights': {0.1: 0.05113190831455035, 0.2: 0.07705573833558801, 0.3: 0.12096721757739311, 0.6: 0.5135061352930738, 0.8: 0.7908166677143281, 0.7: 0.7272187676899062, 0.4: 0.1819252083008504, 0.5: 0.3728054727792405}
}
The function would return a dict after loading the sensitivity file. The keys of the dict are name of parameters in each layer. And the value of key is the information about pruning sensitivity of corresponding layer. In example, pruning 10% filter of the layer corresponding to conv10_expand_weights would lead to 0.65% degradation of model performance. The details could be seen at: [Sensitivity analysis](https://github.com/PaddlePaddle/PaddleSlim/blob/release/2.0-alpha/docs/zh_cn/algo/algo.md)
+```
+
+ The function would return a dict after loading the sensitivity file. The keys of the dict are name of parameters in each layer. And the value of key is the information about pruning sensitivity of corresponding layer. In example, pruning 10% filter of the layer corresponding to conv10_expand_weights would lead to 0.65% degradation of model performance. The details could be seen at: [Sensitivity analysis](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/algo/algo.md#2-%E5%8D%B7%E7%A7%AF%E6%A0%B8%E5%89%AA%E8%A3%81%E5%8E%9F%E7%90%86)
Enter the PaddleOCR root directory，perform sensitivity analysis on the model with the following command：
```bash
-
python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model="your trained model" Global.save_model_dir=./output/prune_model/
-
```
diff --git a/deploy/slim/quantization/README.md b/deploy/slim/quantization/README.md
index 62bc408f5eeda6d8366834200e8d8a20d1dc82cd..8d3f779e0028a62d8396601166283f0ee54d43a7 100644
--- a/deploy/slim/quantization/README.md
+++ b/deploy/slim/quantization/README.md
@@ -1,12 +1,12 @@
-## 介绍
+# PP-OCR模型量化
复杂的模型有利于提高模型的性能，但也导致模型中存在一定冗余，模型量化将全精度缩减到定点数减少这种冗余，达到减少模型计算复杂度，提高模型推理性能的目的。
模型量化可以在基本不损失模型的精度的情况下，将FP32精度的模型参数转换为Int8精度，减小模型参数大小并加速计算，使用量化后的模型在移动端等部署时更具备速度优势。
本教程将介绍如何使用飞桨模型压缩库PaddleSlim做PaddleOCR模型的压缩。
[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim) 集成了模型剪枝、量化（包括量化训练和离线量化）、蒸馏和神经网络搜索等多种业界常用且领先的模型压缩功能，如果您感兴趣，可以关注并了解。
-在开始本教程之前，建议先了解[PaddleOCR模型的训练方法](../../../doc/doc_ch/quickstart.md)以及[PaddleSlim](https://paddleslim.readthedocs.io/zh_CN/latest/index.html)
+在开始本教程之前，建议先了解[PaddleOCR模型的训练方法](../../../doc/doc_ch/training.md)以及[PaddleSlim](https://paddleslim.readthedocs.io/zh_CN/latest/index.html)
## 快速开始
diff --git a/deploy/slim/quantization/README_en.md b/deploy/slim/quantization/README_en.md
index d3bf12d625b076c7bc18016bc9973d1212b3d70b..3f1fe67c9aa3b0b95ee006d97e39e3ce6a19ca22 100644
--- a/deploy/slim/quantization/README_en.md
+++ b/deploy/slim/quantization/README_en.md
@@ -1,5 +1,5 @@
-## Introduction
+# PP-OCR Models Quantization
Generally, a more complex model would achieve better performance in the task, but it also leads to some redundancy in the model.
Quantization is a technique that reduces this redundancy by reducing the full precision data to a fixed number,
diff --git a/doc/doc_ch/environment.md b/doc/doc_ch/environment.md
index 3a266c4bb8fe5516f844bea9f0aa21359d51660e..23bec4b978ab34f144a2ec7256e09412f5440646 100644
--- a/doc/doc_ch/environment.md
+++ b/doc/doc_ch/environment.md
@@ -1,20 +1,19 @@
# 运行环境准备
-Windows和Mac用户推荐使用Anaconda搭建Python环境，Linux用户建议使用docker搭建PyThon环境。
+Windows和Mac用户推荐使用Anaconda搭建Python环境，Linux用户建议使用docker搭建Python环境。
推荐环境：
-- PaddlePaddle >= 2.0.0 (2.1.2)
-- python3.7
+- PaddlePaddle >= 2.1.2
+- Python 3.7
- CUDA10.1 / CUDA10.2
- CUDNN 7.6
-如果对于Python环境熟悉的用户可以直接跳到第2步安装PaddlePaddle。
+> 如果您已经安装Python环境，可以直接参考[PaddleOCR快速开始](./quickstart.md)
* [1. Python环境搭建](#1)
+ [1.1 Windows](#1.1)
+ [1.2 Mac](#1.2)
+ [1.3 Linux](#1.3)
-* [2. 安装PaddlePaddle](#2)
@@ -212,7 +211,7 @@ Linux用户可选择Anaconda或Docker两种方式运行。如果你熟悉Docker
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：
@@ -311,21 +310,3 @@ sudo nvidia-docker run --name ppocr -v $PWD:/paddle --shm-size=64G --network=hos
# ctrl+P+Q可退出docker 容器，重新进入docker 容器使用如下命令
sudo docker container exec -it ppocr /bin/bash
```
-
-
-
-## 2. 安装PaddlePaddle
-
-- 如果您的机器安装的是CUDA9或CUDA10，请运行以下命令安装
-
-```bash
-python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
-```
-
-- 如果您的机器是CPU，请运行以下命令安装
-
-```bash
-python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
-```
-
-更多的版本需求，请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
diff --git a/doc/doc_ch/quickstart.md b/doc/doc_ch/quickstart.md
index 1e0d914140072416710a1b37d72ea88a038793ba..d2126192764fa32c7c7a3651b463b8b23240ea6c 100644
--- a/doc/doc_ch/quickstart.md
+++ b/doc/doc_ch/quickstart.md
@@ -1,6 +1,9 @@
# PaddleOCR快速开始
-- [1. 安装PaddleOCR whl包](#1)
+- [1. 安装](#1)
+ - [1.1 安装PaddlePaddle](#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)
@@ -9,10 +12,35 @@
- [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](#222)
+- [3.小结](#3)
-## 1. 安装PaddleOCR whl包
+## 1. 安装
+
+
+
+### 1.1 安装PaddlePaddle
+
+> 如果您没有基础的Python运行环境，请参考[运行环境准备](./environment.md)。
+
+- 您的机器安装的是CUDA9或CUDA10，请运行以下命令安装
+
+ ```bash
+ python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
+ ```
+
+- 您的机器是CPU，请运行以下命令安装
+
+ ```bash
+ python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
+ ```
+
+更多的版本需求，请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
+
+
+
+### 1.2 安装PaddleOCR whl包
```bash
pip install "paddleocr>=2.0.1" # 推荐使用2.0.1+版本
@@ -257,3 +285,11 @@ im_show = draw_structure_result(image, result,font_path=font_path)
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')
```
+
+
+
+## 3. 小结
+
+通过本节内容，相信您已经熟练掌握PaddleOCR whl包的使用方法并获得了初步效果。
+
+PaddleOCR是一套丰富领先实用的OCR工具库，打通数据、模型训练、压缩和推理部署全流程，因此在[下一节](./paddleOCR_overview.md)中我们将首先为您介绍PaddleOCR的全景图，然后克隆PaddleOCR项目，正式开启PaddleOCR的应用之旅。
diff --git a/doc/doc_en/environment_en.md b/doc/doc_en/environment_en.md
index fc87f10c104628df0268bc6f8910c5914aeba225..6521d3c4144aa579be2075d14826e9dcb9ad9dd6 100644
--- a/doc/doc_en/environment_en.md
+++ b/doc/doc_en/environment_en.md
@@ -1,18 +1,19 @@
# Environment Preparation
-Windows and Mac users are recommended to use Anaconda to build a Python environment, and Linux users are recommended to use docker to build a Python environment. If you are familiar with the Python environment, you can skip to step 2 to install PaddlePaddle.
+Windows and Mac users are recommended to use Anaconda to build a Python environment, and Linux users are recommended to use docker to build a Python environment.
Recommended working environment:
-- PaddlePaddle >= 2.0.0 (2.1.2)
+- PaddlePaddle >= 2.1.2
- Python 3.7
- CUDA 10.1 / CUDA 10.2
- cuDNN 7.6
+> If you already have a Python environment installed, you can skip to [PaddleOCR Quick Start](./quickstart_en.md).
+
* [1. Python Environment Setup](#1)
+ [1.1 Windows](#1.1)
+ [1.2 Mac](#1.2)
+ [1.3 Linux](#1.3)
-* [2. Install PaddlePaddle 2.0](#2)
@@ -330,21 +331,3 @@ You can also visit [DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags
# ctrl+P+Q to exit docker, to re-enter docker using the following command:
sudo docker container exec -it ppocr /bin/bash
```
-
-
-
-## 2. Install PaddlePaddle 2.0
-
-- If you have CUDA 9 or CUDA 10 installed on your machine, please run the following command to install
-
-```bash
-python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
-```
-
-- If you have no available GPU on your machine, please run the following command to install the CPU version
-
-```bash
-python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
-```
-
-For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/install/quick) for operation.
diff --git a/doc/doc_en/quickstart_en.md b/doc/doc_en/quickstart_en.md
index 240a4ba11f3b7df0c518c841d9acee0ae88fcfa8..e44345a8e65f6efc94f83604590d980e052f2abd 100644
--- a/doc/doc_en/quickstart_en.md
+++ b/doc/doc_en/quickstart_en.md
@@ -1,7 +1,9 @@
# PaddleOCR Quick Start
-+ [1. Install PaddleOCR Whl Package](#1-install-paddleocr-whl-package)
++ [1. Installation](#1installation)
+ + [1.1 Install PaddlePaddle](#11-install-paddlepaddle)
+ + [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)
@@ -10,12 +12,35 @@
+ [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 Layout Analysis](#222-layoutAnalysis)
+* [3. Summary](#3)
+
+## 1. Installation
-
+
-## 1. Install PaddleOCR Whl Package
+### 1.1 Install PaddlePaddle
+
+> If you do not have a Python environment, please refer to [Environment Preparation](./environment_en.md).
+
+- If you have CUDA 9 or CUDA 10 installed on your machine, please run the following command to install
+
+ ```bash
+ python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
+ ```
+
+- If you have no available GPU on your machine, please run the following command to install the CPU version
+
+ ```bash
+ python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
+ ```
+
+For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/install/quick) for operation.
+
+
+
+### 1.2 Install PaddleOCR Whl Package
```bash
pip install "paddleocr>=2.0.1" # Recommend to use version 2.0.1+
@@ -248,3 +273,11 @@ im_show = draw_structure_result(image, result,font_path=font_path)
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')
```
+
+
+
+## 3. Summary
+
+In this section, you have mastered the use of PaddleOCR whl packages and obtained results.
+
+PaddleOCR is a rich and practical OCR tool library that opens up the whole process of data, model training, compression and inference deployment, so in the [next section](./paddleOCR_overview_en.md) we will first introduce you to the overview of PaddleOCR, and then clone the PaddleOCR project to start the application journey of PaddleOCR.