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_wired_table_cell_det \
    -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/JUU_5wJWVo4PcmJhSdIo3.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_wired_table_cell_det")
output = model.predict("JUU_5wJWVo4PcmJhSdIo3.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': 'JUU_5wJWVo4PcmJhSdIo3.png', 'page_index': None, 'boxes': [{'cls_id': 0, 'label': 'cell', 'score': 0.9719462394714355, 'coordinate': [98.776054, 48.676155, 235.74197, 94.76812]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9706293344497681, 'coordinate': [235.65723, 48.66303, 473.31378, 94.746185]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9692592620849609, 'coordinate': [235.62718, 164.7009, 473.3329, 211.70175]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9682302474975586, 'coordinate': [98.61444, 164.80591, 235.63733, 211.60106]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9662815928459167, 'coordinate': [1.914098, 48.64288, 98.82235, 94.75366]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9643649458885193, 'coordinate': [1.8260963, 164.74123, 98.64024, 211.56848]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9605159759521484, 'coordinate': [98.783226, 117.873886, 235.74089, 141.91118]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9604074358940125, 'coordinate': [98.77425, 94.79676, 235.80171, 117.937065]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9603073596954346, 'coordinate': [98.788315, 1.8037335, 235.8512, 24.844206]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9592577815055847, 'coordinate': [235.70949, 94.7883, 473.3138, 117.90771]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9591122269630432, 'coordinate': [98.85015, 24.80603, 235.73082, 48.770897]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9586214423179626, 'coordinate': [235.62253, 1.8327671, 473.30493, 24.799725]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9583646059036255, 'coordinate': [235.7168, 117.81723, 473.26074, 141.87694]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9580551385879517, 'coordinate': [98.747986, 141.79, 235.71774, 164.90057]}, {'cls_id': 0, 'label': 'cell', 'score': 0.957258939743042, 'coordinate': [235.6782, 24.70515, 473.0595, 48.79732]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9568949937820435, 'coordinate': [1.8317447, 94.74939, 98.85935, 117.94785]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9563664793968201, 'coordinate': [1.8571337, 1.8207415, 98.98403, 24.901613]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9562588334083557, 'coordinate': [235.67096, 141.72911, 473.3746, 164.82388]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9557535648345947, 'coordinate': [1.922168, 117.84509, 98.85703, 141.85947]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9551460146903992, 'coordinate': [1.8364778, 141.7853, 98.83259, 164.88046]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9547295570373535, 'coordinate': [2.0152304, 24.793072, 98.84856, 48.75716]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9525823593139648, 'coordinate': [235.63931, 211.63988, 473.2472, 254.16182]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9454454779624939, 'coordinate': [98.62049, 211.4913, 235.57971, 254.40237]}, {'cls_id': 0, 'label': 'cell', 'score': 0.9410758018493652, 'coordinate': [1.9204835, 211.48651, 98.601524, 254.9897]}]}}

The visualized image is as follows:

For details about usage command and descriptions of parameters, please refer to the Document.

Pipeline Usage

The ability of a single model is limited. But the pipeline consists of several models can provide more capacity to resolve difficult problems in real-world scenarios.

General Table Recognition V2 Pipeline

The general table recognition V2 pipeline is used to solve table recognition tasks by extracting information from images and outputting it in HTML or Excel format. And there are 8 modules in the pipeline:

• Table Classification Module
• Table Structure Recognition Module
• Table Cell Detection Module
• Text Detection Module