RT-DETR-L_wireless_table_cell_det

Introduction

The Table Cell Detection Module is a key component of the table recognition task, responsible for locating and marking each cell region in table images. The performance of this module directly affects the accuracy and efficiency of the entire table recognition process. The Table Cell Detection Module typically outputs bounding boxes for each cell region, which are then passed as input to the table recognition pipeline for further processing.

Note: The accuracy of RT-DETR-L_wireless_table_cell_det comes from the results of joint testing with RT-DETR-L_wired_table_cell_det.

Installation

1. PaddlePaddle

Please refer to the following commands to install PaddlePaddle using pip:

# for CUDA11.8
python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/

# for CUDA12.6
python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/

# for CPU
python -m pip install paddlepaddle==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/

For details about PaddlePaddle installation, please refer to the PaddlePaddle official website.

2. PaddleOCR

Install the latest version of the PaddleOCR inference package from PyPI:

python -m pip install paddleocr

Model Usage

You can quickly experience the functionality with a single command:

paddleocr table_cells_detection \
    --model_name RT-DETR-L_wireless_table_cell_det \
    -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/6rfhb-CXOHowonjpBsaUJ.png

You can also integrate the model inference of the table classification module into your project. Before running the following code, please download the sample image to your local machine.

from paddleocr import TableCellsDetection
model = TableCellsDetection(model_name="RT-DETR-L_wireless_table_cell_det")
output = model.predict("6rfhb-CXOHowonjpBsaUJ.png", threshold=0.3, batch_size=1)
for res in output:
    res.print(json_format=False)
    res.save_to_img("./output/")
    res.save_to_json("./output/res.json")

After running, the obtained result is as follows:

{'res': {'input_path': '6rfhb-CXOHowonjpBsaUJ.png', 'page_index': None, 'boxes': [{'cls_id': 0, 'label': 'cell', 'score': 0.9398849606513977, 'coordinate': [54.36941, 112.458046, 199.20259, 148.8335]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9389436841011047, 'coordinate': [54.376297, 38.66652, 200.09431, 75.04275]}, {'cls_id': 0, 'label': 'cell', 'score': 0.93695068359375, 'coordinate': [54.526768, 75.07727, 199.69261, 112.47577]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9276502132415771, 'coordinate': [256.82742, 112.23729, 327.20367, 148.69609]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9260919690132141, 'coordinate': [392.2286, 112.35808, 494.87323, 148.67969]}, {'cls_id': 0, 'label': 'cell', 'score': 0.926089882850647, 'coordinate': [55.078747, 148.77213, 198.78673, 181.62665]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9243109822273254, 'coordinate': [256.32922, 74.816475, 327.04968, 112.294014]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9232685565948486, 'coordinate': [54.62298, 6.616625, 199.83049, 38.849678]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9232298135757446, 'coordinate': [327.01968, 112.26065, 392.36826, 148.74333]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9225671291351318, 'coordinate': [256.76163, 39.040295, 326.9102, 74.86264]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9212655425071716, 'coordinate': [326.59286, 74.8661, 392.7218, 112.223015]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9207153916358948, 'coordinate': [392.2682, 74.9181, 494.8996, 112.21204]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9201209545135498, 'coordinate': [393.05807, 39.280144, 494.52887, 74.76607]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9167036414146423, 'coordinate': [326.6303, 38.908886, 392.46747, 74.80093]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9165226817131042, 'coordinate': [198.91599, 112.36962, 256.72226, 148.70464]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9159488081932068, 'coordinate': [200.06506, 38.73822, 256.86224, 74.968956]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9144055843353271, 'coordinate': [199.15344, 74.948166, 256.92688, 112.3458]}, {'cls_id': 0, 'label': 'cell', 'score': 0.909517228603363, 'coordinate': [256.9021, 148.65999, 327.34952, 180.787]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9079439043998718, 'coordinate': [392.5967, 148.63753, 494.56372, 180.72824]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9076585173606873, 'coordinate': [393.64462, 6.3321157, 494.12646, 38.97421]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9043015837669373, 'coordinate': [256.7985, 6.373327, 326.6927, 39.124607]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9015249609947205, 'coordinate': [327.21558, 148.66805, 392.69656, 180.74384]}, {'cls_id': 0, 'label': 'cell', 'score': 0.8990758061408997, 'coordinate': [199.04855, 6.3791466, 256.9587, 38.893078]}, {'cls_id': 0, 'label': 'cell', 'score': 0.8976367712020874, 'coordinate': [326.987, 6.264301, 393.08954, 39.058624]}, {'cls_id': 0, 'label': 'cell', 'score': 0.8959962129592896, 'coordinate': [198.89633, 148.7314, 256.86224, 181.1719]}, {'cls_id': 0, 'label': 'cell', 'score': 0.8942931294441223, 'coordinate': [7.233109, 112.34024, 55.069206, 148.63686]}, {'cls_id': 0, 'label': 'cell', 'score': 0.8866638541221619, 'coordinate': [7.6031237, 75.04754, 54.86649, 112.31445]}, {'cls_id': 0, 'label': 'cell', 'score': 0.8835263848304749, 'coordinate': [7.8346314, 38.471584, 54.338577, 75.0842]}, {'cls_id': 0, 'label': 'cell', 'score': 0.8768432140350342, 'coordinate': [6.3656106, 148.65721, 55.30119, 181.48982]}, {'cls_id': 0, 'label': 'cell', 'score': 0.8766786456108093, 'coordinate': [8.270618, 6.590586, 54.000782, 38.58467]}]}}

The visualized image is as follows:

For details about usage command and descriptions