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+# Real5-OmniDocBench
+
+**Real5-OmniDocBench** is a brand-new benchmark oriented toward real-world scenarios, which we constructed based on the OmniDocBench v1.5 dataset. The dataset comprises five distinct scenarios: Scanning, Curving, Screen-photo, Light Variation, and Skewing. Apart from the Scanning category, all images were manually acquired via handheld mobile devices to closely simulate real-world conditions. Each subset maintains a one-to-one correspondence with the original OmniDocBench, strictly adhering to its ground-truth annotations and evaluation protocols. Given its empirical and realistic nature, this dataset serves as a rigorous benchmark for assessing the robustness of document parsing models in practical applications. 
+
+---
+
+## Key Features
+
+### 1. Real-world Scenarios
+Real5-OmniDocBench contains five challenging and representative photographic scenarios:
+* **Scanning**: Images captured by scanning devices, simulating flat, clean document scans.
+* **Curving**: Photos of documents with visible page curvatures, mimicking book spine or folding distortions.
+* **Screen-photo**: Photographs of screens displaying documents, introducing moiré patterns and reflections.
+* **Lighting-variation**: Images taken under varied lighting conditions, including shadows and glare.
+* **Skewing-variation**: Documents photographed at an angle, resulting in perspective distortion.
+
+### 2. Comprehensive Coverage
+For each scenario, the dataset contains **1,355 images**, corresponding exactly to the original pages in the OmniDocBench evaluation set. This enables direct, controlled comparison of model performance across different real-world conditions.
+
+### 3. High-Quality Realism
+All images are captured manually under real-world conditions. Care was taken to cover typical distortions and artifacts encountered in mobile or camera-based document digitization.
+
+### 4. Evaluation Protocol Compatibility
+The evaluation scripts and metrics from OmniDocBench (e.g., Normalized Edit Distance, BLEU, METEOR, TEDS, COCODet) are fully compatible and directly applicable to Real5-OmniDocBench.
+
+---
+
+## Dataset Structure
+
+The dataset follows the directory structure shown below:
+
+```text
+Real5-OmniDocBench/
+├── Real5-OmniDocBench-curving/
+│   ├── book_en_[搬书匠#20][HTML5 Canvas].2011.英文版_page_208.png
+│   └── ...
+├── Real5-OmniDocBench-lighting-variation/
+│   └── ...
+├── Real5-OmniDocBench-scaning/
+│   └── ...
+├── Real5-OmniDocBench-screen-photo/
+│   └── ...
+└── Real5-OmniDocBench-skewing-variation/
+    └── ...
+```
+
+## Usage & Evaluation
+
+* **Input:** For each scenario, models should take the corresponding set of 1,355 images as input.  
+* **Output:** Model predictions should follow the same format as OmniDocBench, e.g., Markdown for end-to-end parsing.  
+* **Evaluation:** Use the official OmniDocBench evaluation scripts and metrics for assessment. Direct comparison across scenarios is encouraged to measure model robustness.
+
+---
+
+
+## Benchmark Results
+
+To illustrate the impact of real-world visual distortions on document parsing, we evaluated several mainstream models across all five scenarios in Real5-OmniDocBench. The same metrics as OmniDocBench are used: **Overall↑**, **TextEdit↓**, **FormulaCDM↑**, **TableTEDS↑**, and **Reading OrderEdit↓**.
+
+### 1. Scanning
+
+*In the scanning scenario, the models’ overall performance remains high, with PaddleOCR-VL-1.5 typically achieving the best results.*
+
+---
+
+### 2. Curving
+
+*Curved documents introduce moderate challenges, causing a slight performance drop, but specialized VLMs maintain competitive accuracy.*
+
+---
+
+### 3. Screen-photo
+
+---
+
+### 4. Lighting-variation
+
+*Severe lighting variations present significant challenges, resulting in a noticeable performance drop for most models.*
+
+---
+
+### 5. Skewing-variation
+*Skewed documents also impact accuracy, but top-performing models still provide reasonable results.*
+
+
+---
+
+## Acknowledgements
+
+Real5-OmniDocBench is based on the original OmniDocBench dataset and adopts its evaluation protocols. We thank the authors of [OmniDocBench](https://github.com/opendatalab/OmniDocBench) for their foundational work.
+
+---
+
+## Citation
+
+If you use Real5-OmniDocBench in your research, please cite our dataset and refer also to the original OmniDocBench paper.