Document parsing is essential for analyzing complex document structures and extracting fine-grained information, supporting numerous downstream applications. However, existing methods often require integrating multiple independent models to handle various parsing tasks, leading to high complexity and maintenance overhead. To address this, we propose DocFusion, a lightweight generative model with only 0.28B parameters. It unifies task representations and achieves collaborative training through an improved objective function. Experiments reveal and leverage the mutually beneficial interaction among recognition tasks, and integrating recognition data significantly enhances detection performance. The final results demonstrate that DocFusion achieves state-of-the-art (SOTA) performance across four key tasks.
@article{arxiv.2412.12505,
title = {DocFusion: A Unified Framework for Document Parsing Tasks},
author = {Mingxu Chai and Ziyu Shen and Chong Zhang and Yue Zhang and Xiao Wang and Shihan Dou and Jihua Kang and Jiazheng Zhang and Qi Zhang},
journal= {arXiv preprint arXiv:2412.12505},
year = {2025}
}