SLANet_plus Open-source Table Structure Recognition Model - Quickly Convert Table Images to Editable HTML Format

Slanet Plus

Developed by PaddlePaddle

SLANet_plus is a model for table structure recognition that can convert non-editable table images into editable table formats (such as HTML). It plays an important role in the table recognition system and can effectively improve the accuracy and efficiency of table recognition.

Text Recognition Supports Multiple LanguagesOpen Source License:Apache-2.0 #Table structure recognition #HTML conversion #Multi-module integration

Downloads 1,121

Release Time : 6/6/2025

Model Overview

SLANet_plus is a deep learning model focused on table structure recognition. It can accurately identify the positions of rows, columns, and cells in a table and convert non-editable table images into editable HTML formats. This model provides key support in the table recognition system and can be integrated into various document processing workflows.

Model Features

High-precision table structure recognition

Can accurately identify the positions of rows, columns, and cells in a table and convert non-editable table images into editable HTML formats

Multi-module integration pipeline

Provides a general table recognition V2 pipeline and a PP-StructureV3 pipeline, integrating multiple modules such as table classification, structure recognition, text detection, and recognition

Efficient inference

The model storage size is only 6.9M, and it has good inference speed on both GPU and CPU. The GPU inference time is about 140ms

End-to-end solution

Supports the complete process from image input to structured output and can output multiple formats such as HTML and Excel

Model Capabilities

Table structure recognition

Table image conversion

HTML format output

Excel format output

Multi-module collaborative processing

Use Cases

Document processing

Financial statement recognition

Convert scanned financial statement images into editable HTML or Excel formats

Accurately identify the table structure and retain the original data relationships

Reimbursement form processing

Automatically identify the table information in the reimbursement form and output it in a structured manner

The recognition accuracy is 63.69%, which can significantly reduce manual data entry work

Data digitization

Digitization of historical archives

Convert the table content in paper archives into editable digital formats

Retain the original table structure for subsequent data analysis and processing

🚀 SLANet_plus

SLANet_plus is a model for table structure recognition, which can convert non - editable table images into editable table formats, improving the accuracy and efficiency of table recognition.

🚀 Quick Start

Table structure recognition is a crucial part of table recognition systems. It can transform non - editable table images into editable formats like HTML. The performance of this module directly impacts the accuracy and efficiency of the entire table recognition system. The following is a comparison of SLANet_plus's performance:

Model	Accuracy (%)	GPU Inference Time (ms) [Normal Mode / High Performance Mode]	CPU Inference Time (ms) [Normal Mode / High Performance Mode]	Model Storage Size (M)
SLANet_plus	63.69	140.29 / 140.29	195.39 / 195.39	6.9 M

✨ Features

High Accuracy: Achieves an accuracy of 63.69% in table structure recognition.
Fast Inference: With relatively short inference times on both GPU and CPU.
Pipeline Compatibility: Can be integrated into different table recognition pipelines.

📦 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 SLANet_plus \
    -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 TableStructureRecognition
model = TableStructureRecognition(model_name="SLANet_plus")
output = model.predict(input="UHf7jONQ3a18cszdL_Wuo.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': '6rfhb-CXOHowonjpBsaUJ.png', 'page_index': None, 'bbox': [[1, 2, 64, 2, 64, 41, 1, 41], [52, 1, 199, 1, 198, 38, 51, 38], [182, 1, 253, 1, 254, 40, 184, 40], [248, 1, 323, 1, 324, 41, 249, 41], [314, 1, 384, 1, 385, 40, 315, 40], [389, 2, 493, 2, 493, 45, 388, 44], [2, 42, 50, 42, 50, 77, 2, 77], [65, 42, 176, 42, 175, 77, 64, 77], [187, 40, 251, 40, 249, 79, 185, 79], [252, 41, 319, 41, 319, 80, 251, 80], [318, 40, 379, 40, 380, 78, 318, 78], [385, 39, 497, 39, 497, 84, 384, 83], [2, 82, 50, 82, 50, 118, 2, 118], [63, 80, 182, 80, 181, 114, 62, 114], [189, 80, 250, 80, 249, 114, 187, 114], [253, 80, 319, 80, 319, 114, 252, 114], [320, 78, 378, 79, 378, 114, 320, 114], [395, 77, 496, 78, 496, 118, 394, 118], [2, 117, 49, 118, 50, 155, 2, 155], [65, 115, 180, 115, 179, 151, 64, 151], [191, 115, 249, 115, 248, 150, 189, 150], [254, 115, 318, 115, 318, 150, 253, 150], [321, 114, 377, 114, 378, 150, 321, 150], [396, 113, 495, 113, 495, 154, 394, 153], [1, 153, 56, 153, 57, 192, 1, 191], [68, 152, 175, 152, 175, 189, 67, 189], [189, 152, 249, 152, 249, 188, 188, 188], [252, 152, 317, 152, 318, 188, 252, 188], [320, 150, 377, 151, 378, 188, 321, 187], [393, 150, 494, 151, 494, 193, 391, 192]], 'structure': ['<html>', '<body>', '<table>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '</table>', '</body>', '</html>'], 'structure_score': 0.99635947}}

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

Advanced Usage

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 with default config, which uses the SLANeXt_wired and SLANeXt_wireless to predict the table structure:

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/")

Then, if you want to use the SLANet_plus model for table recognition, just change the model name and use the end - to - end prediction mode as below:

paddleocr table_recognition_v2 -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/mabagznApI1k9R8qFoTLc.png  \
    --use_doc_orientation_classify False  \
    --use_doc_unwarping False \
    --wired_table_structure_recognition_model_name SLANet_plus \ 
    --use_e2e_wired_table_rec_model True \
    --wireless_table_structure_recognition_model_name SLANet_plus \
    --use_e2e_wireless_table_rec_model True \
    --save_path ./output \
    --device gpu:0

from paddleocr import TableRecognitionPipelineV2

pipeline = TableRecognitionPipelineV2(
    use_doc_orientation_classify=False,
    use_doc_unwarping=False, 
    wired_table_structure_recognition_model_name=SLANet_plus,  ## for wired table recognition
    wireless_table_structure_recognition_model_name=SLANet_plus,  ## for wireless table recognition
)
output = pipeline.predict(
    "https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/mabagznApI1k9R8qFoTLc.png",
    use_e2e_wired_table_rec_model=True,  ## for wired table recognition
    use_e2e_wireless_table_rec_model=True,  ## for wireless table recognition
    )
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 \
    --wired_table_structure_recognition_model_name SLANet_plus \ 
    --use_e2e_wired_table_rec_model True \
    --wireless_table_structure_recognition_model_name SLANet_plus \
    --use_e2e_wireless_table_rec_model True \
    --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(
    wired_table_structure_recognition_model_name=SLANet_plus,  ## for wired table recognition
    wireless_table_structure_recognition_model_name=SLANet_plus,  ## for wireless table recognition
    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",
    use_e2e_wired_table_rec_model=True,  ## for wired table recognition
    use_e2e_wireless_table_rec_model=True,  ## for wireless table recognition
    )
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

The default model used in pipeline is SLANeXt_wired and SLANeXt_wireless, so it is needed that specifing to SLANet_plus by argument. For details about usage command and descriptions of parameters, please refer to the Document.

📚 Documentation

For more detailed information about usage commands and parameter descriptions, please refer to the following documents:

📄 License

This project is licensed under the Apache - 2.0 license.

📎 Links

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