MeDocVL: A Visual Language Model for Medical Document Understanding and Parsing

Wenjie Wang; Wei Wu; Ying Liu; Yuan Zhao; Xiaole Lv; Liang Diao; Zengjian Fan; Wenfeng Xie; Ziling Lin; De Shi; Lin Huang; Kaihe Xu; Hong Li

arXiv · 2602.06402

MeDocVL: A Visual Language Model for Medical Document Understanding and Parsing

Wenjie Wang·Wei Wu·Ying Liu·Yuan Zhao·Xiaole Lv·Liang Diao·Zengjian Fan·Wenfeng Xie·Ziling Lin·De Shi·Lin Huang·Kaihe Xu·Hong Li

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Abstract

Medical document OCR is challenging due to complex layouts, domain-specific terminology, and noisy annotations, while requiring strict field-level exact matching. Existing OCR systems and general-purpose vision-language models often fail to reliably parse such documents. We propose MeDocVL, a post-trained vision-language model for query-driven medical document parsing. Our framework combines Training-driven Label Refinement to construct high-quality supervision from noisy annotations, with a Noise-aware Hybrid Post-training strategy that integrates reinforcement learning and supervised fine-tuning to achieve robust and precise extraction. Experiments on medical invoice benchmarks show that MeDocVL consistently outperforms conventional OCR systems and strong VLM baselines, achieving state-of-the-art performance under noisy supervision.

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BibTeX RIS

Wenjie Wang, Wei Wu, Ying Liu, Yuan Zhao, Xiaole Lv, Liang Diao, Zengjian Fan, Wenfeng Xie, Ziling Lin, De Shi, Lin Huang, Kaihe Xu, Hong Li. 2026-02-06. MeDocVL: A Visual Language Model for Medical Document Understanding and Parsing. https://arxiv.org/abs/2602.06402

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