SLANeXt_wired Open-source Table Recognition Model - Convert table images to editable HTML format for free

Slanext Wired

Developed by PaddlePaddle

SLANeXt_wired is a deep learning model for table structure recognition, which can convert non - editable table images into editable table formats (such as HTML).

Text Recognition Supports Multiple LanguagesOpen Source License:Apache-2.0 #Table structure recognition #HTML conversion #Document processing

Downloads 1,141

Release Time : 6/6/2025

Model Overview

This model is an important part of the table recognition system, focusing on identifying the positions of rows, columns, and cells in the table, outputting the HTML code of the table area, and providing input for the subsequent table recognition process.

Model Features

High - precision table structure recognition

It can accurately identify the positions of rows, columns, and cells in the table and output structured HTML code.

Integration into the complete process

It can be used as part of the general table recognition V2 process and the PP - StructureV3 process, working in collaboration with other modules.

Support for multiple output formats

It supports saving the recognition results in multiple formats such as JSON, HTML, and Excel.

Model Capabilities

Table structure recognition

HTML code generation

Table image analysis

Use Cases

Document processing

Reimbursement form processing

Recognize the table structure in the reimbursement form and extract information such as department, reimbursee, and amount.

Output structured HTML code for subsequent data processing and analysis.

Financial statement analysis

Convert paper financial statements into editable digital formats.

Accurately recognize the table structure and preserve the original data relationships.

Data entry automation

Digitization of paper forms

Convert scanned paper forms into editable electronic forms.

Reduce the workload of manual data entry and improve data accuracy.

🚀 SLANeXt_wired

SLANeXt_wired is a model for table structure recognition. It can convert non - editable table images into editable table formats, playing a crucial role in improving the efficiency and accuracy of table recognition systems.

✨ Features

High - accuracy Recognition: SLANeXt_wired achieves an accuracy of 69.65% in joint testing with SLANeXt_wireless.
Efficient Installation: It can be quickly installed through PaddlePaddle and PaddleOCR.
Multiple Usage Modes: Support both single - command experience and project integration.

Property	Details
Model Type	SLANeXt_wired
Accuracy	69.65%
GPU Inference Time	--
CPU Inference Time	--
Model Storage Size	351M

Note: The accuracy of SLANeXt_wired comes from the results of joint testing with SLANeXt_wireless.

📦 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

💻 Usage Examples

Basic Usage

You can quickly experience the functionality with a single command:

paddleocr table_structure_recognition \
    --model_name SLANeXt_wired \
    -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/JUU_5wJWVo4PcmJhSdIo3.png

Advanced Usage

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 TableStructureRecognition
model = TableStructureRecognition(model_name="SLANeXt_wired")
output = model.predict(input="JUU_5wJWVo4PcmJhSdIo3.png", batch_size=1)
for res in output:
    res.print(json_format=False)
    res.save_to_json("./output/res.json")

After running, the obtained result is as follows:

{'res': {'input_path': 'JUU_5wJWVo4PcmJhSdIo3.png', 'page_index': None, 'bbox': [[12, 4, 96, 5, 87, 43, 11, 40], [188, 4, 276, 5, 261, 50, 174, 48], [352, 4, 477, 4, 477, 57, 341, 55], [9, 36, 133, 38, 126, 95, 8, 93], [211, 40, 282, 40, 269, 104, 198, 103], [330, 38, 476, 39, 476, 106, 320, 106], [49, 72, 107, 76, 105, 187, 47, 182], [215, 78, 284, 80, 280, 180, 212, 177], [334, 72, 476, 73, 476, 175, 333, 175], [6, 140, 145, 153, 149, 247, 6, 233], [197, 149, 282, 158, 285, 254, 201, 245], [302, 144, 476, 152, 476, 254, 305, 246], [32, 196, 100, 208, 107, 312, 34, 299], [193, 198, 270, 209, 282, 318, 206, 309], [322, 192, 475, 202, 475, 327, 333, 319], [5, 257, 122, 271, 137, 379, 6, 370], [171, 262, 260, 273, 277, 392, 188, 386], [296, 257, 476, 265, 476, 398, 313, 394], [17, 319, 107, 322, 120, 454, 20, 454], [155, 319, 266, 320, 284, 457, 173, 457], [288, 307, 475, 308, 475, 460, 307, 460], [12, 426, 101, 425, 103, 475, 12, 475], [154, 399, 279, 390, 285, 475, 160, 476], [289, 388, 475, 380, 475, 475, 297, 476]], 'structure': ['<html>', '<body>', '<table>', '<thead>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '</thead>', '<tbody>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '</tbody>', '</table>', '</body>', '</html>'], 'structure_score': 0.9998931}}

📚 Documentation

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
Text Recognition Module
Layout Region Detection Module (Optional)
Document Image Orientation Classification Module (Optional)
Text Image Unwarping Module (Optional)

Run a single command to quickly experience the general table recognition V2 pipeline:

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

Results are 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:

image/jpeg

The command - line method is for quick experience. For project integration, also only a few codes are needed as well:

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 details about usage command and descriptions of parameters, 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）

Run a single command to quickly experience the PP - StructureV3 pipeline:

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

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

Just a few lines of code can experience the inference of the pipeline. 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(".mG4tnwfrvECoFMu-S9mxo.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 details about usage command and descriptions of parameters, please refer to the Document.

📄 License

This project is licensed under the Apache - 2.0 license.

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