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SLANeXt

docs/source/en/model_doc/slanext.md

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This model was released on 2025-03-07 and added to Hugging Face Transformers on 2026-03-21.

SLANeXt

<div class="flex flex-wrap space-x-1"> </div>

Overview

SLANeXt_wired and SLANeXt_wireless 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 SLANeXt series model, please refer to the official documentation.

Model Architecture

The SLANeXt series is a new generation of table structure recognition models independently developed by the Baidu PaddlePaddle Vision Team. SLANeXt focuses on table structure recognition, and trains dedicated weights for wired and wireless tables separately. The recognition ability for all types of tables has been significantly improved, especially for wired tables.

Usage

Single input inference

The example below demonstrates how to detect text with PP-OCRV5_Mobile_Det using the [AutoModel].

<hfoptions id="usage"> <hfoption id="AutoModel">
python
import requests
from PIL import Image

from transformers import AutoImageProcessor, AutoModelForTableRecognition


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

image = Image.open(requests.get("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg", stream=True).raw)
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>

SLANeXtConfig

[[autodoc]] SLANeXtConfig

SLANeXtForTableRecognition

[[autodoc]] SLANeXtForTableRecognition

SLANeXtBackbone

[[autodoc]] SLANeXtBackbone

SLANeXtSLAHead

[[autodoc]] SLANeXtSLAHead

SLANeXtImageProcessor

[[autodoc]] SLANeXtImageProcessor