test=documents_fix,test=release/2.3

2021-09-07 10:01:06 +08:00 · 2021-09-07 10:01:06 +08:00 · 5fb116a0e6
parent a83f9aef70
commit 5fb116a0e6
3 changed files with 22 additions and 20 deletions
--- a/README.md
+++ b/README.md
@ -146,7 +146,7 @@ For a new language request, please refer to [Guideline for new language_requests

 [1] PP-OCR is a practical ultra-lightweight OCR system. It is mainly composed of three parts: DB text detection, detection frame correction and CRNN text recognition. The system adopts 19 effective strategies from 8 aspects including backbone network selection and adjustment, prediction head design, data augmentation, learning rate transformation strategy, regularization parameter selection, pre-training model use, and automatic model tailoring and quantization to optimize and slim down the models of each module (as shown in the green box above). The final results are an ultra-lightweight Chinese and English OCR model with an overall size of 3.5M and a 2.8M English digital OCR model. For more details, please refer to the PP-OCR technical article (https://arxiv.org/abs/2009.09941).

-[2] On the basis of PP-OCR, PP-OCRv2 is further optimized in five aspects. The detection model adopts CML(Collaborative Mutual Learning) knowledge distillation strategy and CopyPaste data expansion strategy; The recognition model adopts LCNet lightweight backbone network, U-DML knowledge distillation strategy and enhanced CTC loss function improvement (as shown in the red box above), which further improves the inference speed and prediction effect. For more details, please refer to the technical report of PP-OCRv2 (arXiv link is coming soon).
+[2] On the basis of PP-OCR, PP-OCRv2 is further optimized in five aspects. The detection model adopts CML(Collaborative Mutual Learning) knowledge distillation strategy and CopyPaste data expansion strategy. The recognition model adopts LCNet lightweight backbone network, U-DML knowledge distillation strategy and enhanced CTC loss function improvement (as shown in the red box above), which further improves the inference speed and prediction effect. For more details, please refer to the technical report of PP-OCRv2 (arXiv link is coming soon).



--- a/doc/doc_ch/environment.md
+++ b/doc/doc_ch/environment.md
@ -1,4 +1,7 @@
 # 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。
+
+如果对于Python环境熟悉的用户可以直接跳到第2步安装PaddlePaddle。

 * [1. Python环境搭建](#1)
  + [1.1 Windows](#1.1)
@ -63,9 +66,9 @@
  ```

    <img src="../install/windows/conda_list_env.png" alt="create environment" width="600" align="center"/>
-  
-  
-  
+
+
+

 以上anaconda环境和python环境安装完毕

@ -80,9 +83,9 @@
  - 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="center"/>
-  
+
  - 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
@ -177,7 +180,7 @@ Linux用户可选择Anaconda或Docker两种方式运行。如果你熟悉Docker
 - 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
@ -185,22 +188,22 @@ Linux用户可选择Anaconda或Docker两种方式运行。如果你熟悉Docker

    - 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
    ```

@ -210,7 +213,7 @@ Linux用户可选择Anaconda或Docker两种方式运行。如果你熟悉Docker
    - 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
@ -277,13 +280,13 @@ Linux用户可选择Anaconda或Docker两种方式运行。如果你熟悉Docker
  # 激活paddle_env环境
  conda activate paddle_env
  ```
-  
+

 以上anaconda环境和python环境安装完毕

 #### 1.3.2 Docker环境配置

-**注意：第一次使用这个镜像，会自动下载该镜像，请耐心等待。**
+**注意：第一次使用这个镜像，会自动下载该镜像，请耐心等待。您也可以访问[DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/)获取与您机器适配的镜像。**

 ```bash
 # 切换到工作目录下
@ -297,8 +300,6 @@ sudo docker run --name ppocr -v $PWD:/paddle --network=host -it paddlepaddle/pad
 如果使用CUDA10，请运行以下命令创建容器，设置docker容器共享内存shm-size为64G，建议设置32G以上
 sudo nvidia-docker run --name ppocr -v $PWD:/paddle --shm-size=64G --network=host -it paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82 /bin/bash

-您也可以访问[DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/)获取与您机器适配的镜像。
-
 # 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
 ```
--- a/doc/doc_ch/quickstart.md
+++ b/doc/doc_ch/quickstart.md
@ -2,7 +2,7 @@


 - [PaddleOCR快速开始](#paddleocr)
-  
+
  + [1. 安装PaddleOCR whl包](#1)
  * [2. 便捷使用](#2)
    + [2.1 命令行使用](#21)
@ -166,7 +166,7 @@ paddleocr --image_dir=./table/1.png --type=structure

  ```
  /output/table/1/
-    └─ res.txt										
+    └─ 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
@ -183,7 +183,7 @@ paddleocr --image_dir=./table/1.png --type=structure

  大部分参数和paddleocr whl包保持一致，见 [whl包文档](./whl.md)

-  
+

 <a name="22"></a>
 ### 2.2 Python脚本使用
@ -232,6 +232,7 @@ im_show.save('result.jpg')
    <img src="../imgs_results/whl/11_det_rec.jpg" width="800">
 </div>
 <a name="222"></a>
+
 #### 2.2.2 版面分析

 ```python