Tools for parsing table structures based ONNXRuntime.
Project description
rapid-table
1. Install package by pypi.
⚠️Attention: After rapid_table>=v0.1.0, you need to install rapidocr_onnxruntime package firstly.
pip install rapidocr_onnxruntime
pip install rapid-table
2. Run by script.
-
RapidTable has the default
model_pathvalue, you can set the different value ofmodel_pathto use different models, e.g.table_engine = RapidTable(model_path='ch_ppstructure_mobile_v2_SLANet.onnx') -
See details, for README_Table .
-
📌
table.jpgsource: linkfrom rapid_table import RapidTable from rapidocr_onnxruntime import RapidOCR table_engine = RapidTable() ocr_engine = RapidOCR() img_path = 'test_images/table.jpg' ocr_result, _ = ocr_engine(img_path) table_html_str, _ = table_engine(img_path, ocr_result) print(table_html_str)
3. Run by command line.
-
Usage:
$ 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.
-
Example:
$ rapid_table -v -img test_images/table.jpg
4. Result.
- Return value.
<html><body><table><tr><td>Methods</td><td></td><td></td><td></td><td>FPS</td></tr><tr><td>SegLink [26]</td><td>70.0</td><td>86d><td.0</td><td>77.0</td><td>8.9</td></tr><tr><td>PixelLink [4]</td><td>73.2</td><td>83.0</td><td>77.8</td><td></td></tr><tr><td>TextSnake [18]</td><td>73.9</td><td>83.2</td><td>78.3</td><td>1.1</td></tr><tr><td>TextField [37]</td><td>75.9</td><td>87.4</td><td>81.3</td><td>5.2</td></tr><tr><td>MSR[38]</td><td>76.7</td><td>87.87.4</td><td>81.7</td><td></td></tr><tr><td>FTSN [3]</td><td>77.1</td><td>87.6</td><td>82.0</td><td></td></tr><tr><td>LSE[30]</td><td>81.7</td><td>84.2</td><td>82.9</td><><ttd></td></tr><tr><td>CRAFT [2]</td><td>78.2</td><td>88.2</td><td>82.9</td><td>8.6</td></tr><tr><td>MCN[16]</td><td>79</td><td>88</td><td>83</td><td></td></tr><tr><td>ATRR</>[35]</td><td>82.1</td><td>85.2</td><td>83.6</td><td></td></tr><tr><td>PAN [34]</td><td>83.8</td><td>84.4</td><td>84.1</td><td>30.2</td></tr><tr><td>DB[12]</td><td>79.2</t91/d><td>91.5</td><td>84.9</td><td>32.0</td></tr><tr><td>DRRG[41]</td><td>82.30</td><td>88.05</td><td>85.08</td><td></td></tr><tr><td>Ours (SynText)</td><td>80.68</td><td>85<t..40</td><td>82.97</td><td>12.68</td></tr><tr><td>Ours (MLT-17)</td><td>84.54</td><td>86.62</td><td>85.57</td><td>12.31</td></tr></table></body></html>
- Visualize result.
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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
For details, see Rapid Table
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