VirtualLUO/Wild_OmniDocBench

Wild-OmniDocBench

A Real-World Captured Document Parsing Benchmark for Robustness Evaluation

中文版 • Paper • GitHub • HuggingFace

Overview

Wild-OmniDocBench is a benchmark for evaluating document parsing robustness under real-world captured conditions. It is derived from OmniDocBench by converting scanned/digital documents into naturally captured images through controlled physical simulation, including printing, deformation, and photography under diverse lighting conditions.

Unlike standard benchmarks that rely on clean scanned or digital-born pages, Wild-OmniDocBench introduces realistic artifacts such as:

• Geometric distortions (perspective shifts, bends, wrinkles)
• Illumination variations (directional, uneven, low-light)
• Screen capture artifacts (moire patterns, reflections)
• Environmental interference (background overlays, shadows)

Note: The current release of Wild-OmniDocBench corresponds to OmniDocBench v1.5. We are currently processing the extended portions for v1.6 and will release them in a future update.

Benchmark Statistics

Item	Details
Total Images	1,350
Source	Real-world captured variant of OmniDocBench
Document Types	Books, Textbooks, Papers, PPTs, Newspapers, Notes, Exams, Magazines, Financial Reports, etc.
Capture Methods	(i) Print + physical deformation + photography; (ii) Screen display + re-capture
Annotations	Inherited from OmniDocBench (full structural and reading-order annotations)

Data Format

Directory Structure

Wild_OmniDocBench/
├── README.md                   # English README
├── README_ZH.md                # Chinese README
├── wild_omnidocbench.zip       # Benchmark images (1,350 JPGs)
└── assets/
    └── overview.png            # Overview figure

Images

After unzipping wild_omnidocbench.zip, images are named following the OmniDocBench convention:

{doc_type}_{language}_{source}_{page}.jpg

For example: book_en_A.Concise.Introduction.to.Linear.Algebra_page_065.jpg

Evaluation

Wild-OmniDocBench uses the same annotation format and evaluation protocol as OmniDocBench. To evaluate on Wild-OmniDocBench:

1. Obtain annotations and evaluation scripts from the official OmniDocBench repository:

   https://github.com/opendatalab/OmniDocBench
   

2. Replace the image source with Wild-OmniDocBench images (from wild_omnidocbench.zip).

3. Run evaluation following the OmniDocBench protocol. Metrics include:

   • Overall Score (↑)
   • Text Edit Distance (↓)
   • Formula CDM (↑)
   • Table TEDS (↑)
   • Reading Order Edit Distance (↓)

Key Results

Performance degradation from OmniDocBench to Wild-OmniDocBench (from the DocHumming paper):

Model	Type	Overall (Origin)	Overall (Wild)	Degradation
DocHumming (1B)	End2End	93.75	87.03	−6.72
dots.ocr (3B)	End2End	88.41	78.01	−10.40
Qwen3-VL (235B)	General	89.15	79.69	−9.46
MinerU2.5 (1.2B)	Modular	90.67	70.91	−19.76
PaddleOCR-VL (0.9B)	Modular	91.93	72.19	−19.74

End-to-end models exhibit significantly less degradation than modular cascaded pipelines under real-world capture conditions.

Citation

@misc{li2026towardsrealworlddocument,
      title={Towards Real-World Document Parsing via Realistic Scene Synthesis and Document-Aware Training},
      author={Gengluo Li and Pengyuan Lyu and Chengquan Zhang and Huawen Shen and Liang Wu and Xingyu Wan and Gangyan Zeng and Han Hu and Can Ma and Yu Zhou},
      year={2026},
      journal={arXiv preprint arXiv:2603.23885},
      url={https://arxiv.org/abs/2603.23885},
}

Acknowledgements

Wild-OmniDocBench is built upon OmniDocBench. We thank the OmniDocBench team for providing the original annotations and evaluation framework.

License

This benchmark is released for research purposes only.