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

arXiv: https://arxiv.org/abs/2603.04205 · HTML: https://mlsanity.com/paper/2603.04205/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Document understanding and multimodal RAG, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 4 Mar 2026.

## Placed in

- Multimodal LLMs / vision-language models / [Document understanding and multimodal RAG](https://mlsanity.com/mllm/document-understanding-and-multimodal-rag/)

## Builds on (most cited first, 12 of 12)

- [ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction](https://mlsanity.com/paper/2103.10213/) (2021)
- [Image-based table recognition: data, model, and evaluation](https://mlsanity.com/paper/1911.10683/) (2019)
- [olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models](https://mlsanity.com/paper/2502.18443/) (2025)
- [MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing](https://mlsanity.com/paper/2509.22186/) (2025)
- [PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model](https://mlsanity.com/paper/2510.14528/) (2025)
- [MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradigm](https://mlsanity.com/paper/2506.05218/) (2025)
- [PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing](https://mlsanity.com/paper/2601.21957/) (2026)
- [Image Over Text: Transforming Formula Recognition Evaluation with Character Detection Matching](https://mlsanity.com/paper/2409.03643/) (2024)
- [DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks](https://mlsanity.com/paper/2405.04408/) (2024)
- [WildDoc: How Far Are We from Achieving Comprehensive and Robust Document Understanding in the Wild?](https://mlsanity.com/paper/2505.11015/) (2025)
- [Ocean-OCR: Towards General OCR Application via a Vision-Language Model](https://mlsanity.com/paper/2501.15558/) (2025)
- [DocPTBench: Benchmarking End-to-End Photographed Document Parsing and Translation](https://mlsanity.com/paper/2511.18434/) (2025)

## Built on by (most cited first, 12 of 12)

- [PaddleOCR-VL-1.6: Expanding the Frontier of Document Parsing with Under-Optimized Region Refinement and Progressive Post-Training](https://mlsanity.com/paper/2606.03264/) (2026)
- [MDPBench: A Benchmark for Multilingual Document Parsing in Real-World Scenarios](https://mlsanity.com/paper/2603.28130/) (2026)
- [How Far Is Document Parsing from Solved? PureDocBench: A Source-Traceable Benchmark across Clean, Degraded, and Real-World Settings](https://mlsanity.com/paper/2605.07492/) (2026)
- [CC-OCR V2: Fine-Grained Attribution of LMM Failures in Real-World Visual Document Understanding](https://mlsanity.com/paper/2605.03903/) (2026)
- [RT-DocLayout: Real-Time End-to-End Document Layout Analysis with Reading Order in the Wild](https://mlsanity.com/paper/2606.23344/) (2026)
- [GDP.pdf: Benchmarking Grounded Multimodal Reasoning over Professional PDF Documents](https://mlsanity.com/paper/2607.11192/) (2026)
- [Parser-Oriented Structural Refinement for a Stable Layout Interface in Document Parsing](https://mlsanity.com/paper/2604.02692/) (2026)
- [How Do Document Parsers Break? Auditing Structural Vulnerability in Document Intelligence](https://mlsanity.com/paper/2605.19309/) (2026)
- [MUDIDI: A Two-Stage Framework for Multilingual Dictionary Digitization with Language Models](https://mlsanity.com/paper/2606.09435/) (2026)
- [From Diagnosis to Correction: Benchmarking and Improving Real-World Table Parsing](https://mlsanity.com/paper/2608.09842/) (2026)
- [WildHandBench: A Benchmark for Handwritten Text Understanding that Challenges MLLMs and Humans](https://mlsanity.com/paper/2608.22959/) (2026)
- [Xiaomi-OCR-0 Technical Report](https://mlsanity.com/paper/2609.36136/) (2026)

## Abstract (arXiv)

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.