---
title: SLANeXt_wireless_onnx
canonical_url: "https://www.modelscope.cn/models/PaddlePaddle/SLANeXt_wireless_onnx"
md_url: "https://www.modelscope.cn/models/PaddlePaddle/SLANeXt_wireless_onnx.md"
repository: PaddlePaddle/SLANeXt_wireless_onnx
last_updated: 2026-06-12
license: apache-2.0
library_name:
  - onnx
language:
  - en
  - zh
downloads: 12
stars: 0
tags:
  - OCR
  - PaddlePaddle
  - PaddleOCR
  - table_structure_recognition
---

# SLANeXt_wireless_onnx

> SLANeXt_wireless_onnx - PaddlePaddle 在 ModelScope 开源的模型。Table structure recognition is an important component of table recognition systems, capable of converting non-editable table images into editable table formats (such as HTML). The goal of table…

PaddlePaddle/SLANeXt_wireless_onnx 是 ModelScope 魔搭社区上的机器学习模型，采用 apache-2.0 许可。

- **Repository**: PaddlePaddle/SLANeXt_wireless_onnx
- **License**: apache-2.0
- **Tags**: OCR, PaddlePaddle, PaddleOCR, table_structure_recognition
- **Downloads**: 12
- **Stars**: 0
- **Last updated**: 2026-06-12

Source: https://www.modelscope.cn/models/PaddlePaddle/SLANeXt_wireless_onnx

---

# SLANeXt_wireless

## Introduction

Table structure recognition is an important component of table recognition systems, capable of converting non-editable table images into editable table formats (such as HTML). The goal of table structure recognition is to identify the positions of rows, columns, and cells in tables. The performance of this module directly affects 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 passed as input to the table recognition pipeline for further processing.

<table>
<tr>
<th>Model</th>
<th>Accuracy (%)</th>
<th>GPU Inference Time (ms)<br/>[Normal Mode / High Performance Mode]</th>
<th>CPU Inference Time (ms)<br/>[Normal Mode / High Performance Mode]</th>
<th>Model Storage Size (M)</th>
</tr>
<tr>
<td>SLANeXt_wireless</td>
<td>69.65</td>
<td>--</td>
<td>--</td>
<td>351M</td>
</tr>
</table>

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

## Model Usage

### Install Dependencies

```shell
pip install -U paddleocr
pip install -U onnxruntime-gpu
```

### CLI Usage

```shell
paddleocr table_structure_recognition -i ./demo.jpg --model_name SLANeXt_wireless --engine onnxruntime
```

### Python API Usage

```python
from paddleocr import TableStructureRecognition

model = TableStructureRecognition(
    model_name="SLANeXt_wireless",
    engine="onnxruntime",
)
output = model.predict("./demo.jpg", batch_size=1)
for res in output:
    res.print()
    res.save_to_json(save_path="./output/res.json")
```