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+# Wild-OmniDocBench
+
+**A Real-World Captured Document Parsing Benchmark for Robustness Evaluation**
+
+<p align="center">
+  <a href="https://huggingface.co/datasets/VirtualLUO/Wild_OmniDocBench/blob/main/README_ZH.md">中文版</a> •
+  <a href="https://arxiv.org/abs/2603.23885">Paper</a> •
+  <a href="https://github.com/VirtualLUOUCAS/Wild_OmniDocBench">GitHub</a> •
+  <a href="https://huggingface.co/datasets/VirtualLUO/Wild_OmniDocBench">HuggingFace</a>
+</p>
+
+## Overview
+
+**Wild-OmniDocBench** is a benchmark for evaluating document parsing robustness under real-world captured conditions. It is derived from [OmniDocBench](https://github.com/opendatalab/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.
+
+<p align="center">
+  <img src="assets/overview.png" width="90%" alt="Wild-OmniDocBench Construction">
+</p>
+
+## 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](https://github.com/opendatalab/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
+
+```bibtex
+@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](https://github.com/opendatalab/OmniDocBench). We thank the OmniDocBench team for providing the original annotations and evaluation framework.
+
+## License
+
+This benchmark is released for **research purposes only**.