---
license: mit
datasets:
- dimtri009/SLANet-1M_dataset
language:
- en
base_model:
- PaddlePaddle/SLANet
tags:
- table-recognition
- table-structure-recognition
- slanet-1m
---


# SLANet-1M: A Lightweight Model for Table Recognition

![License](https://img.shields.io/badge/License-MIT-blue.svg)
![Python](https://img.shields.io/badge/python-3.9%2B-blue)
![Framework](https://img.shields.io/badge/framework-PyTorch-orange)

---

## 🧾 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](https://aclanthology.org/2025.swisstext-1.9/).

---

## 🚀 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  

---

## 📦 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 |

---

Please cite us: 

```
@inproceedings{romaric-etal-2025-slanet,
    title = "{SLAN}et-1{M}: A Lightweight and Efficient Model for Table Recognition with Minimal Computational Cost",
    author = "Romaric, Nguinwa Mbakop Dimitri  and
      Petrucci, Andrea  and
      Marinai, Simone  and
      Hennebert, Jean",
    editor = {Gerber, Jonathan  and
      Cieliebak, Mark  and
      Tuggener, Don  and
      H{\"u}rlimann, Manuela},
    booktitle = "Proceedings of the 10th edition of the Swiss Text Analytics Conference",
    month = may,
    year = "2025",
    address = "Winterthur, Switzerland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.swisstext-1.9/",
    pages = "89--102"
}
```