## Rapid Table
#### 简介和说明
- 该部分主要是做文档类图像的表格结构还原。模型来源：[PaddleOCR 表格识别](https://github.com/PaddlePaddle/PaddleOCR/blob/133d67f27dc8a241d6b2e30a9f047a0fb75bebbe/ppstructure/table/README_ch.md)
- 具体来说，就是分析给定的表格图像，将表格还原为对应的html格式。
- 目前支持两种类别的表格识别模型：中文和英文表格识别模型，具体可参见下面表格：
| 模型类型 | 模型名称 | 模型大小 |
|:---:|:---:|:---:|
| 英文 | `en_ppstructure_mobile_v2_SLANet.onnx` | 7.3M |
| 中文 | `ch_ppstructure_mobile_v2_SLANet.onnx` | 7.4M |
- 模型下载地址为：[百度网盘](https://pan.baidu.com/s/1PI9fksW6F6kQfJhwUkewWg?pwd=p29g) | [Google Drive](https://drive.google.com/drive/folders/1DAPWSN2zGQ-ED_Pz7RaJGTjfkN2-Mvsf?usp=sharing)
#### 使用方式
1. pip安装
- 由于模型较小，预先将英文表格识别模型(`en_ppstructure_mobile_v2_SLANet.onnx`)打包进了whl包内，如果做英文表格识别，可直接安装使用
```bash
$ pip install rapid-table
```
2. python脚本运行
```python
import cv2
from rapid_table import RapidTable
# RapidTable类提供model_path参数，可以自行指定上述2个模型，默认是en_ppstructure_mobile_v2_SLANet.onnx
# table_engine = RapidTable(model_path='ch_ppstructure_mobile_v2_SLANet.onnx')
table_engine = RapidTable()
img = cv2.imread('test_images/table.jpg')
table_html_str, _ = table_engine(img)
print(table_html_str)
```
3. 终端运行
- 用法:
```bash
$ rapid_table -h
usage: rapid_table [-h] [-v] -img IMG_PATH [-m MODEL_PATH]
optional arguments:
-h, --help show this help message and exit
-v, --vis Wheter to visualize the layout results.
-img IMG_PATH, --img_path IMG_PATH
Path to image for layout.
-m MODEL_PATH, --model_path MODEL_PATH
The model path used for inference.
```
- 示例:
```bash
$ rapid_table -v -img test_images/table.jpg
```
4. 结果
- 返回结果
```html
| Methods | | | | FPS |
| SegLink [26] | 70.0 | 86d>| 77.0 | 8.9 | |
| PixelLink [4] | 73.2 | 83.0 | 77.8 | |
| TextSnake [18] | 73.9 | 83.2 | 78.3 | 1.1 |
| TextField [37] | 75.9 | 87.4 | 81.3 | 5.2 |
| MSR[38] | 76.7 | 87.87.4 | 81.7 | |
| FTSN [3] | 77.1 | 87.6 | 82.0 | |
| LSE[30] | 81.7 | 84.2 | 82.9 | <>
| CRAFT [2] | 78.2 | 88.2 | 82.9 | 8.6 |
| MCN[16] | 79 | 88 | 83 | |
| ATRR[35] | 82.1 | 85.2 | 83.6 | |
| PAN [34] | 83.8 | 84.4 | 84.1 | 30.2 |
| DB[12] | 79.2 | 91.5 | 84.9 | 32.0 |
| DRRG[41] | 82.30 | 88.05 | 85.08 | |
| Ours (SynText) | 80.68 | 85| 82.97 | 12.68 | |
| Ours (MLT-17) | 84.54 | 86.62 | 85.57 | 12.31 |
```
- 可视化结果
| Methods | | | | FPS |
| SegLink [26] | 70.0 | 86d>| 77.0 | 8.9 | |
| PixelLink [4] | 73.2 | 83.0 | 77.8 | |
| TextSnake [18] | 73.9 | 83.2 | 78.3 | 1.1 |
| TextField [37] | 75.9 | 87.4 | 81.3 | 5.2 |
| MSR[38] | 76.7 | 87.87.4 | 81.7 | |
| FTSN [3] | 77.1 | 87.6 | 82.0 | |
| LSE[30] | 81.7 | 84.2 | 82.9 | <>
| CRAFT [2] | 78.2 | 88.2 | 82.9 | 8.6 |
| MCN[16] | 79 | 88 | 83 | |
| ATRR[35] | 82.1 | 85.2 | 83.6 | |
| PAN [34] | 83.8 | 84.4 | 84.1 | 30.2 |
| DB[12] | 79.2 | 91.5 | 84.9 | 32.0 |
| DRRG[41] | 82.30 | 88.05 | 85.08 | |
| Ours (SynText) | 80.68 | 85| 82.97 | 12.68 | |
| Ours (MLT-17) | 84.54 | 86.62 | 85.57 | 12.31 |