Authors:Bangbang Zhou, Hangdi Xing, Yifan Chen, Jianjun Xu, Qi Zheng, Feiyu Gao, Zhibo Yang, Shuai Bai, Ming Yan, Jieping Ye, Hongtao Xie
Abstract:Document parsing converts visually rich documents into machine-readable structured representations, forming a crucial foundation for information systems. Although many benchmarks have been proposed for document parsing, they remain inadequate for realistic scenarios. Existing benchmarks either focus on specific tasks or assess only single-page, text-centric settings, making them insufficient for practical multi-page parsing. Moreover, they lack fine-grained evaluation of semantic continuity, hierarchical structure recovery, and visual content preservation. To address these gaps, we propose MPDocBench-Parse, a benchmark for multi-page document parsing in real-world applications. It contains 433 manually annotated documents with 3,246 pages, covering 15 document types in English and Chinese, with diverse layout styles, and supports document-level end-to-end evaluation. We further design a comprehensive protocol for content fidelity and logical structure, covering text, table, and formula recognition, truncated text and table merging, figure extraction, reading order, and heading hierarchy recovery. Experiments show that, while existing models perform well on basic text extraction, they still suffer clear limitations in semantic continuity integration, visual content parsing, and hierarchical structure recovery. MPDocBench-Parse provides a unified foundation for advancing document parsing toward more realistic scenarios.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.22100 [cs.AI] |
| (or arXiv:2605.22100v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.22100 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Bangbang Zhou [view email]
[v1]
Thu, 21 May 2026 07:36:41 UTC (25,615 KB)