English | [简体中文](README_ch.md)

- [Getting Started](#getting-started)
  - [1.  Introduction](#1)
  - [2. Install](#2)
    - [2.1 Installation dependencies](#2.1)
    - [2.2 Install PaddleOCR](#2.2)
  - [3. Quick Start](#3)

<a name="1"></a>

## 1.  Introduction

Layout recovery means that after OCR recognition, the content is still arranged like the original document pictures, and the paragraphs are output to word document in the same order.

Layout recovery combines [layout analysis](../layout/README.md)、[table recognition](../table/README.md) to better recover images, tables, titles, etc.
The following figure shows the result：

<div align="center">
<img src="../docs/table/recovery.jpg"  width = "700" />
</div>
<a name="2"></a>

## 2. Install

<a name="2.1"></a>

### 2.1 Install dependencies

- **(1) Install PaddlePaddle**

```bash
python3 -m pip install --upgrade pip

# GPU installation
python3 -m pip install "paddlepaddle-gpu>=2.2" -i https://mirror.baidu.com/pypi/simple

# CPU installation
python3 -m pip install "paddlepaddle>=2.2" -i https://mirror.baidu.com/pypi/simple

````

For more requirements, please refer to the instructions in [Installation Documentation](https://www.paddlepaddle.org.cn/install/quick).

<a name="2.2"></a>

### 2.2 Install PaddleOCR

- **(1) Download source code**

```bash
[Recommended] git clone https://github.com/PaddlePaddle/PaddleOCR

# If the pull cannot be successful due to network problems, you can also choose to use the hosting on the code cloud:
git clone https://gitee.com/paddlepaddle/PaddleOCR

# Note: Code cloud hosting code may not be able to synchronize the update of this github project in real time, there is a delay of 3 to 5 days, please use the recommended method first.
````

- **(2) Install recovery's `requirements`**

```bash
python3 -m pip install -r ppstructure/recovery/requirements.txt
````

<a name="3"></a>

## 3. Quick Start

```python
cd PaddleOCR/ppstructure

# download model
mkdir inference && cd inference
# Download the detection model of the ultra-lightweight English PP-OCRv3 model and unzip it
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar && tar xf ch_PP-OCRv3_det_infer.tar
# Download the recognition model of the ultra-lightweight English PP-OCRv3 model and unzip it
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar && tar xf  ch_PP-OCRv3_rec_infer.tar
# Download the ultra-lightweight English table inch model and unzip it
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
# Download the layout model of publaynet dataset and unzip it
wget 
https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_layout_infer.tar && tar picodet_lcnet_x1_0_layout_infer.tar
cd ..
# run
python3 predict_system.py \
    --image_dir=./docs/table/1.png \
    --det_model_dir=inference/en_PP-OCRv3_det_infer \
    --rec_model_dir=inference/en_PP-OCRv3_rec_infe \
    --rec_char_dict_path=../ppocr/utils/en_dict.txt \
    --output=../output/ \
    --table_model_dir=inference/ch_ppstructure_mobile_v2.0_SLANet_infer \
    --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt \
    --table_max_len=488 \
    --layout_model_dir=inference/picodet_lcnet_x1_0_layout_infer \
    --layout_dict_path=../ppocr/utils/dict/layout_dict/layout_publaynet_dict.txt \
    --vis_font_path=../doc/fonts/simfang.ttf \
    --recovery=True \
		--save_pdf=False
```

After running, the docx  of each picture will be saved in the directory specified by the output field

Recovery table to Word code[table_process.py] reference：https://github.com/pqzx/html2docx.git