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*This model was contributed to Hugging Face Transformers on 2026-04-22.*

# SLANet


## Overview

**SLANet** and **SLANet_plus** are part of a series of dedicated lightweight models for table structure recognition, focusing on accurately recognizing table structures in documents and natural scenes. For more details about the SLANet series model, please refer to the [official documentation](https://www.paddleocr.ai/latest/en/version3.x/module_usage/table_structure_recognition.html).

## Model Architecture

SLANet is a table structure recognition model developed by Baidu PaddlePaddle Vision Team. The model significantly improves the accuracy and inference speed of table structure recognition by adopting a CPU-friendly lightweight backbone network PP-LCNet, a high-low-level feature fusion module CSP-PAN, and a feature decoding module SLA Head that aligns structural and positional information.

## Usage

### Single input inference

The example below demonstrates how to detect text with SLANet using the [`AutoModel`].

<hfoptions id="usage">
<hfoption id="AutoModel">

```python
from io import BytesIO

import httpx
from PIL import Image

from transformers import AutoImageProcessor, AutoModelForTableRecognition


model_path="PaddlePaddle/SLANet_plus_safetensors"
model = AutoModelForTableRecognition.from_pretrained(model_path, device_map="auto")
image_processor = AutoImageProcessor.from_pretrained(model_path)

image = Image.open(BytesIO(httpx.get(image_url).content))
inputs = image_processor(images=image, return_tensors="pt").to(model.device)
outputs = model(**inputs)

results = image_processor.post_process_table_recognition(outputs)

print(result['structure'])
print(result['structure_score'])
```

</hfoption>
</hfoptions>

## SLANetConfig

[[autodoc]] SLANetConfig

## SLANetForTableRecognition

[[autodoc]] SLANetForTableRecognition

## SLANetBackbone

[[autodoc]] SLANetBackbone

## SLANetSLAHead

[[autodoc]] SLANetSLAHead

