### Install wired_table_rec

Source: https://rapidai.github.io/TableStructureRec/docs/install_usage/wired_table_rec

Install the library using pip.

```bash
pip install wired_table_rec
```

--------------------------------

### Install lineless_table_rec

Source: https://rapidai.github.io/TableStructureRec/docs/install_usage/lineless_table_rec

Use pip to install the library.

```bash
pip install lineless_table_rec
```

--------------------------------

### Example Recognition Output

Source: https://rapidai.github.io/TableStructureRec/docs/install_usage/wired_table_rec

The expected HTML output format for a recognized table.

```html
<html>

<body>
    <table>
        <tr>
            <td rowspan="2">名称</td>
            <td rowspan="2">产量（吨）</td>
            <td colspan="2">环比</td>
        </tr>
        <tr>
            <td>增长量（吨）</td>
            <td>增长率（%)</td>
        </tr>
        <tr>
            <td>荔枝</td>
            <td>11</td>
            <td></td>
            <td>10</td>
        </tr>
        <tr>
            <td>芒果</td>
            <td></td>
            <td></td>
            <td>-10</td>
        </tr>
        <tr>
            <td>香蕉</td>
            <td></td>
            <td></td>
            <td>20</td>
        </tr>
    </table>
</body>

</html>
```

--------------------------------

### Perform Table Recognition in Python

Source: https://rapidai.github.io/TableStructureRec/docs/install_usage/lineless_table_rec

Initialize the recognition class, process an image, and save the resulting HTML and visualization files.

```python
import os

from lineless_table_rec import LinelessTableRecognition
from lineless_table_rec.utils_table_recover import (
    format_html,
    plot_rec_box,
    plot_rec_box_with_logic_info,
)

output_dir = "outputs"
img_path = "tests/test_files/lineless_table_recognition.jpg"
table_rec = LinelessTableRecognition()

html, elasp, polygons, logic_points, ocr_res = table_rec(img_path)
print(f"cost: {elasp:.5f}")

complete_html = format_html(html)
os.makedirs(os.path.dirname(f"{output_dir}/table.html"), exist_ok=True)

with open(f"{output_dir}/table.html", "w", encoding="utf-8") as file:
    file.write(complete_html)

plot_rec_box_with_logic_info(
    img_path, f"{output_dir}/table_rec_box.jpg", logic_points, polygons
)
plot_rec_box(img_path, f"{output_dir}/ocr_box.jpg", ocr_res)
```

--------------------------------

### Run Recognition via Command Line

Source: https://rapidai.github.io/TableStructureRec/docs/install_usage/wired_table_rec

Execute the recognition process directly from the terminal.

```bash
$ wired_table_rec -img tests/test_files/wired/table_recognition.jpg
```

--------------------------------

### Run Table Recognition via Command Line

Source: https://rapidai.github.io/TableStructureRec/docs/install_usage/lineless_table_rec

Execute the recognition process directly from the terminal by specifying the image path.

```bash
$ lineless_table_rec -img tests/test_files/lineless_table_recognition.jpg
```

--------------------------------

### 安装表格识别推理库

Source: https://rapidai.github.io/TableStructureRec/docs/blog/table_rec_evaluate

安装用于表格识别的推理库。

```bash
pip install rapid_table
pip install lineless_table_rec
pip install wired_table_rec
```

--------------------------------

### 安装评测依赖

Source: https://rapidai.github.io/TableStructureRec/docs/blog/table_rec_evaluate

安装评测所需的工具包和模型推理环境。

```bash
pip install table_recognition_metric
pip install modelscope==1.5.2
pip install rapidocr_onnxruntime==1.3.8
```

--------------------------------

### 执行表格识别评测

Source: https://rapidai.github.io/TableStructureRec/docs/blog/table_rec_evaluate

加载测试数据集并使用指定的表格识别引擎计算TEDS指标。

```python
from modelscope.msdatasets import MsDataset

from rapid_table import RapidTable
from lineless_table_rec import LinelessTableRecognition
from wired_table_rec import WiredTableRecognition

from table_recognition_metric import TEDS

test_data = MsDataset.load(
    "table_recognition",
    namespace="liekkas",
    subset_name="default",
    split="test",
)

# 这里依次更换不同算法实例即可
table_engine = RapidTable()
# table_engine = LinelessTableRecognition()
# table_engine = WiredTableRecognition()
teds = TEDS()

content = []
for one_data in test_data:
    img_path = one_data.get("image:FILE")
    gt = one_data.get("label")

    pred_str, _ = table_engine(img_path)
    scores = teds(gt, pred_str)
    content.append(scores)
    print(f"{img_path}\t{scores:.5f}")

avg = sum(content) / len(content)
print(f'{avg:.5f}')
```

--------------------------------

### Perform Table Recognition

Source: https://rapidai.github.io/TableStructureRec/docs/install_usage/wired_table_rec

Use the WiredTableRecognition class to process an image and retrieve the table structure.

```python
from wired_table_rec import WiredTableRecognition


table_rec = WiredTableRecognition()

img_path = "tests/test_files/wired/table_recognition.jpg"
table_str, elapse = table_rec(img_path)
print(table_str)
print(elapse)
```

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