GraphDoc

<h1 align="center">Graph-based Document Structure Analysis (ICLR'25)</h1> <p align="center"> <a href="https://arxiv.org/pdf/2403.14442.pdf"> <img src="https://img.shields.io/badge/PDF-arXiv-brightgreen" /></a> <a href="https://yufanchen96.github.io/projects/GraphDoc/"> <img src="https://img.shields.io/badge/Project-Homepage-red" /></a> <a href="https://pytorch.org/get-started/previous-versions/#linux-and-windows"> <img src="https://img.shields.io/badge/Framework-PyTorch%201.10.2-orange" /></a> <a href="https://github.com/yufanchen96/GraphDoc/blob/main/LICENSE"> <img src="https://img.shields.io/badge/License-Apache_2.0-blue.svg" /></a> </p>

🏡 Project Homepage

This is the official repository for our ICLR 2025 paper Graph-based Document Structure Analysis. For more results and details, please visit our project homepage.

🔎 Introduction

we construct a relation graph-based document structure analysis dataset (GraphDoc), enabling training models to complete tasks like reading order, hierarchical structure analysis, and complex inter-element relation inference. Furthermore, a document relation graph generator (DRGG) is proposed to address the tasks.

<p align="center"> <img src="assets/GraphDoc.png" width="480" /> </p>

📝 Catalog

• [ ] Graph-based Document Structure Dataset
• [ ] DRGG Model Checkpoints
• [ ] DRGG Model Training Code
• [ ] DRGG Model Evaluation Code

📦 Code and Implementations

Code and Implementation details will come soon!

🌳 Citation

If you find this code useful for your research, please consider citing:

@inproceedings{chen2025graphdoc,
      title={Graph-based Document Structure Analysis}, 
      author={Yufan Chen and Ruiping Liu and Junwei Zheng and Di Wen and Kunyu Peng and Jiaming Zhang and Rainer Stiefelhagen},
      booktitle={ICLR},
      year={2025}
}