Open-source SLANeXt_wireless model - Convert table images to editable HTML format for free

Slanext Wireless

Developed by PaddlePaddle

SLANeXt_wireless is a model for table structure recognition, which can convert non - editable table images into editable HTML format.

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

Downloads 244

Release Time : 6/6/2025

Model Overview

SLANeXt_wireless 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, with an accuracy rate of 69.65%.

Multiple usage methods

Supports single - model usage and pipeline usage composed of multiple models to meet the needs of different scenarios.

Output editable format

Converts table images into editable HTML format for subsequent processing and use.

Model Capabilities

Table structure recognition

Image - to - HTML conversion

Table row and column detection

Table cell positioning

Use Cases

Document processing

Financial statement recognition

Convert scanned financial statement images into editable HTML format

Accurately recognize the table structure and retain the original layout

Data table extraction

Extract data tables from documents and convert them into structured formats

Facilitate data analysis and processing

Office automation

PDF table conversion

Convert tables in PDF into editable format

Improve document processing efficiency

🚀 SLANeXt_wireless

Table structure recognition model that converts non - editable table images into editable formats, enhancing the accuracy and efficiency of table recognition systems.

🚀 Quick Start

Table structure recognition is a crucial part of table recognition systems. It can transform non - editable table images into editable table formats (e.g., HTML). The main goal is to identify the positions of rows, columns, and cells in tables, and its performance directly impacts the accuracy and efficiency of the entire table recognition system. The table structure recognition module usually outputs HTML code for the table area, which is then used as input for further processing in the table recognition pipeline.

Property	Details
Model Type	SLANeXt_wireless
Accuracy (%)	69.65
GPU Inference Time (ms) [Normal Mode / High Performance Mode]	--
CPU Inference Time (ms) [Normal Mode / High Performance Mode]	--
Model Storage Size (M)	351M

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

✨ Features

Converts non - editable table images into editable HTML formats.
Can be used independently or integrated into the PaddleOCR pipeline.
Supports multiple usage scenarios, including single - command experience and project integration.

📦 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 more 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

Model Usage

Basic Usage

You can quickly experience the functionality with a single command:

paddleocr table_structure_recognition \
    --model_name SLANeXt_wireless \
    -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/6rfhb-CXOHowonjpBsaUJ.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_wireless")
output = model.predict(input="6rfhb-CXOHowonjpBsaUJ.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': [[5, 4, 48, 5, 46, 85, 5, 81], [84, 6, 146, 6, 143, 101, 83, 98], [186, 6, 217, 6, 212, 104, 184, 98], [239, 7, 281, 8, 276, 107, 235, 108], [324, 6, 405, 6, 404, 105, 323, 106], [405, 4, 488, 5, 488, 100, 403, 94], [3, 56, 96, 60, 95, 187, 3, 180], [108, 68, 157, 71, 159, 193, 110, 187], [179, 75, 207, 79, 211, 199, 184, 192], [238, 72, 277, 76, 281, 203, 243, 199], [318, 68, 400, 70, 404, 207, 325, 205], [395, 66, 494, 68, 494, 214, 397, 212], [11, 138, 62, 145, 68, 329, 12, 321], [105, 151, 156, 158, 171, 332, 117, 323], [177, 157, 210, 166, 229, 322, 197, 312], [232, 152, 276, 159, 295, 322, 253, 316], [313, 142, 396, 147, 409, 330, 332, 326], [392, 139, 491, 144, 492, 332, 404, 330], [3, 239, 86, 254, 103, 450, 3, 445], [97, 251, 152, 261, 176, 458, 116, 454], [172, 254, 211, 265, 239, 461, 200, 458], [235, 248, 289, 257, 316, 466, 264, 464], [310, 235, 402, 242, 419, 469, 337, 468], [381, 229, 491, 236, 492, 469, 400, 468], [9, 340, 74, 361, 88, 490, 11, 489], [95, 338, 129, 353, 150, 493, 113, 492], [176, 342, 192, 358, 221, 493, 206, 492], [235, 335, 261, 351, 289, 493, 265, 492], [310, 325, 372, 339, 393, 493, 338, 493], [382, 321, 482, 334, 485, 493, 402, 493]], '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': 0.9999998}}

Pipeline 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)

Basic Usage

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

Advanced Usage

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）

Basic Usage

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.

Advanced Usage

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.

📚 Documentation

📄 License

This project is licensed under the Apache - 2.0 license.

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