SLANet-1M: A Lightweight Model for Table Recognition

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🧾 Overview

SLANet-1M is a lightweight convolutional model for table recognition designed to extract table structure and cell content from document images efficiently.
It is trained on over one million synthetic and real-world tables and provides competitive performance compared to transformer-based architectures—while being significantly smaller and faster.

This model was developed as part of a Master’s thesis at the University of Florence and the Swiss AI Center (iCoSys, Fribourg), and presented at SwissText 2025.

The paper is available here.

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🚀 Key Features

• Lightweight architecture (≈9.2M parameters)
• Transformer-free design for CPU-friendly deployment
• Trained on large-scale datasets (PubTabNet + SynthTabNet)
• Compatible with deployment pipelines such as the Core Engine
• Outputs table structure in HTML format

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📦 Model Details

Property	Description
Model Name	SLANet-1M
Architecture	CNN-based (SLANet variant with depthwise separable convolutions)
Parameters	~9.2 million
Input Size	480×480 (RGB)
Output Format	HTML table structure
Training Data	PubTabNet + SynthTabNet (all subsets)
Metrics	S-TEDS: 99.36 on SynthTabNet and 97.36 on PubTabNet

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