RT DETR L Wired Table Cell Det

🚀 RT-DETR-L_wired_table_cell_det

The Table Cell Detection Module is a crucial part of table recognition. It locates and marks each cell region in table images, directly affecting the accuracy and efficiency of the entire recognition process. This module typically outputs bounding boxes for cell regions, which are then used as input for further table recognition processing.

✨ Features

• High accuracy in table cell detection, with a Top1 Acc of 82.7%.
• Supports both GPU and CPU inference, providing different performance modes.
• Can be integrated into various table recognition pipelines.

📦 Installation

1. PaddlePaddle

Install PaddlePaddle using pip with the following commands:

# 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 more details on PaddlePaddle installation, refer to the PaddlePaddle official website.

2. PaddleOCR

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

python -m pip install paddleocr

💻 Usage Examples

Basic Usage

You can quickly test 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 into your project. First, download the sample image to your local machine, then run the following code:

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, you will get a result like this:

{'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 more details about the usage command and parameter descriptions, please refer to the Document.

Pipeline Usage

The ability of a single model is limited. However, a pipeline consisting of several models can provide more capabilities to solve 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. There are 8 modules in the pipeline:

• Table Classification Module
• Table Structure Recognition Module
• Table Cell Detection Module
• Text Detection Module
• Text Recognition Module
• Layout Region Detection Module (Optional)
• Document Image Orientation Classification Module (Optional)
• Text Image Unwarping Module (Optional)

You can quickly test the general table recognition V2 pipeline with a single command:

paddleocr table_recognition_v2 -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/mabagznApI1k9R8qFoTLc.png  \
    --use_doc_orientation_classify False  \
    --use_doc_unwarping False \
    --save_path ./output \
    --device gpu:0

The results will be printed to the terminal:

{'res': {'input_path': 'mabagznApI1k9R8qFoTLc.png', 'page_index': None, 'model_settings': {'use_doc_preprocessor': False, 'use_layout_detection': True, 'use_ocr_model': True}, 'layout_det_res': {'input_path': None, 'page_index': None, 'boxes': [{'cls_id': 8, 'label': 'table', 'score': 0.86655592918396, 'coordinate': [0.0125130415, 0.41920784, 1281.3737, 585.3884]}]}, 'overall_ocr_res': {'input_path': None, 'page_index': None, 'model_settings': {'use_doc_preprocessor': False, 'use_textline_orientation': False}, 'dt_polys': array([[[   9,   21],
        ...,
        [   9,   59]],

       ...,

       [[1046,  536],
        ...,
        [1046,  573]]], dtype=int16), 'text_det_params': {'limit_side_len': 960, 'limit_type': 'max', 'thresh': 0.3, 'box_thresh': 0.6, 'unclip_ratio': 2.0}, 'text_type': 'general', 'textline_orientation_angles': array([-1, ..., -1]), 'text_rec_score_thresh': 0, 'rec_texts': ['部门', '报销人', '报销事由', '批准人：', '单据', '张', '合计金额', '元', '车费票', '其', '火车费票', '飞机票', '中', '旅住宿费', '其他', '补贴'], 'rec_scores': array([0.99958128, ..., 0.99317062]), 'rec_polys': array([[[   9,   21],
        ...,
        [   9,   59]],

       ...,

       [[1046,  536],
        ...,
        [1046,  573]]], dtype=int16), 'rec_boxes': array([[   9, ...,   59],
       ...,
       [1046, ...,  573]], dtype=int16)}, 'table_res_list': [{'cell_box_list': [array([ 0.13052222, ..., 73.08310249]), array([104.43082511, ...,  73.27777413]), array([319.39041221, ...,  73.30439308]), array([424.2436837 , ...,  73.44736794]), array([580.75836265, ...,  73.24003914]), array([723.04370201, ...,  73.22717598]), array([984.67315757, ...,  73.20420387]), array([1.25130415e-02, ..., 5.85419208e+02]), array([984.37072837, ..., 137.02281502]), array([984.26586998, ..., 201.22290352]), array([984.24017417, ..., 585.30775765]), array([1039.90606773, ...,  265.44664314]), array([1039.69549644, ...,  329.30540779]), array([1039.66546714, ...,  393.57319954]), array([1039.5122689 , ...,  457.74644783]), array([1039.55535972, ...,  521.73030403]), array([1039.58612144, ...,  585.09468392])], 'pred_html': '<html><body><table><tbody><tr><td>部门</td><td></td><td>报销人</td><td></td><td>报销事由</td><td></td><td colspan="2">批准人：</td></tr><tr><td colspan="6" rowspan="8"></td><td colspan="2">单据 张</td></tr><tr><td colspan="2">合计金额 元</td></tr><tr><td rowspan="6">其 中</td><td>车费票</td></tr><tr><td>火车费票</td></tr><tr><td>飞机票</td></tr><tr><td>旅住宿费</td></tr><tr><td>其他</td></tr><tr><td>补贴</td></tr></tbody></table></body></html>', 'table_ocr_pred': {'rec_polys': array([[[   9,   21],
        ...,
        [   9,   59]],

       ...,

       [[1046,  536],
        ...,
        [1046,  573]]], dtype=int16), 'rec_texts': ['部门', '报销人', '报销事由', '批准人：', '单据', '张', '合计金额', '元', '车费票', '其', '火车费票', '飞机票', '中', '旅住宿费', '其他', '补贴'], 'rec_scores': array([0.99958128, ..., 0.99317062]), 'rec_boxes': array([[   9, ...,   59],
       ...,
       [1046, ...,  573]], dtype=int16)}}]}}

If save_path is specified, the visualization results will be saved under save_path. The visualization output is shown below:

For project integration, you only need a few lines of code:

from paddleocr import TableRecognitionPipelineV2

pipeline = TableRecognitionPipelineV2(
    use_doc_orientation_classify=False, # Use use_doc_orientation_classify to enable/disable document orientation classification model
    use_doc_unwarping=False, # Use use_doc_unwarping to enable/disable document unwarping module
)
# pipeline = TableRecognitionPipelineV2(use_doc_orientation_classify=True) # Specify whether to use the document orientation classification model with use_doc_orientation_classify
# pipeline = TableRecognitionPipelineV2(use_doc_unwarping=True) # Specify whether to use the text image unwarping module with use_doc_unwarping
# pipeline = TableRecognitionPipelineV2(device="gpu") # Specify the device to use GPU for model inference
output = pipeline.predict("https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/mabagznApI1k9R8qFoTLc.png")
for res in output:
    res.print() ## Print the predicted structured output
    res.save_to_img("./output/")
    res.save_to_xlsx("./output/")
    res.save_to_html("./output/")
    res.save_to_json("./output/")

For more details about the usage command and parameter descriptions, please refer to the Document.

PP-StructureV3

Layout analysis is a technique used to extract structured information from document images. PP-StructureV3 includes the following six modules:

• Layout Detection Module
• General OCR Pipeline
• Document Image Preprocessing Pipeline （Optional）
• Table Recognition Pipeline （Optional）
• Seal Recognition Pipeline （Optional）
• Formula Recognition Pipeline （Optional）

You can quickly test the PP-StructureV3 pipeline with a single command:

paddleocr pp_structurev3 -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/mG4tnwfrvECoFMu-S9mxo.png \
    --use_doc_orientation_classify False \
    --use_doc_unwarping False \
    --use_textline_orientation False \
    --device gpu:0

The results will be printed to the terminal. If save_path is specified, the results will be saved under save_path.

You can also experience the inference of the pipeline with just a few lines of code. Taking the PP-StructureV3 pipeline as an example:

from paddleocr import PPStructureV3

pipeline = PPStructureV3(
    use_doc_orientation_classify=False, # Use use_doc_orientation_classify to enable/disable document orientation classification model
    use_doc_unwarping=False,    # Use use_doc_unwarping to enable/disable document unwarping module
    use_textline_orientation=False, # Use use_textline_orientation to enable/disable textline orientation classification model
    device="gpu:0", # Use device to specify GPU for model inference
    )
output = pipeline.predict("SfxF0X4drBTNGnfFOtZij.png")
for res in output:
    res.print() # Print the structured prediction output
    res.save_to_json(save_path="output") ## Save the current image's structured result in JSON format
    res.save_to_markdown(save_path="output") ## Save the current image's result in Markdown format

For more details about the usage command and parameter descriptions, please refer to the Document.

📚 Documentation

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

📄 License

This project is licensed under the Apache-2.0 license.

Links

• PaddleOCR Repo
• PaddleOCR Documentation