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arxiv: 2603.04205 · v2 · pith:GK4W4FMOnew · submitted 2026-03-04 · 💻 cs.CV

Real5-OmniDocBench: A Full-Scale Physical Reconstruction Benchmark for Robust Document Parsing in the Wild

Changda Zhou , Ziyue Gao , Xueqing Wang , Tingquan Gao , Cheng Cui , Jing Tang , Yi Liu This is my paper
classification 💻 cs.CV
keywords benchmarkdocumentphysicaldigitalfirstfull-scalelackomnidocbench
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While Vision-Language Models (VLMs) achieve near-perfect scores on digital document benchmarks like OmniDocBench, their performance in the unpredictable physical world remains largely unknown due to the lack of controlled yet realistic evaluations. We introduce Real5-OmniDocBench, the first benchmark that performs a full-scale, one-to-one physical reconstruction of the entire OmniDocBench v1.5 (1,355 images) across five critical real-world scenarios: Scanning, Warping, Screen-Photography, Illumination, and Skew. Unlike prior benchmark that either lack digital correspondence or employ partial sampling, our complete ground-truth mapping enables, for the first time, rigorous factor-wise attribution of performance degradation-allowing us to pinpoint whether failures stem from geometric distortions, optical artifacts, or model limitations. Our benchmark establishes a challenging new standard for the community, demonstrating that the 'reality gap' in document parsing is far from closed, and provides a diagnostic tool to guide the development of truly resilient document intelligence.

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Cited by 4 Pith papers

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