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

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 to provide a full-scale, one-to-one physical reconstruction of the complete OmniDocBench v1.5 test set (1,355 images) across five critical real-world scenarios: Scanning, Warping, Screen-Photography, Illumination, and Skew. Unlike prior benchmarks 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. The Real5-OmniDocBench dataset is publicly available at https:// huggingface.co/datasets/PaddlePaddle/Real5-OmniDocBench.