# 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, Warping, Screen-Photography, Illumination, and Skew. 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 and clean document scans. * **Warping**: Photos of documents with visible page curvatures, mimicking distortions caused by book spines or folding. * **Skew**: Documents photographed at an angle, resulting in perspective distortion. * **Screen-Photography**: Photographs of screens displaying documents, introducing moiré patterns and reflections. * **Illumination**: Images taken under varied lighting conditions, including shadows and glare. ### 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-Warping/ │ ├── book_en_[搬书匠#20][HTML5 Canvas].2011.英文版_page_208.png │ └── ... ├── Real5-OmniDocBench-Illumination/ │ └── ... ├── Real5-OmniDocBench-Scanning/ │ └── ... ├── Real5-OmniDocBench-Screen-Photography/ │ └── ... └── Real5-OmniDocBench-Skew/ └── ... ``` ## 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
Model Type Methods Parameters Overall↑ TextEdit↓ FormulaCDM↑ TableTEDS↑ Reading OrderEdit↓
Pipeline Tools Maker-1.8.2 - 70.27 0.223 77.03 56.05 0.238
PP-StructureV3 - 84.68 0.094 84.34 79.06 0.092
General VLMs GPT-5.2 - 84.43 0.142 85.68 81.78 0.109
Qwen2.5-VL-72B 72B 86.19 0.110 86.14 83.41 0.114
Gemini-2.5 Pro - 89.25 0.073 87.44 87.62 0.098
Qwen3-VL-235B-A22B-Instruct 235B 89.43 0.059 89.01 85.19 0.066
Gemini-3 Pro - 89.47 0.071 88.16 87.37 0.078
Specialized VLMs Dolphin 322M 72.16 0.154 64.58 67.27 0.130
Dolphin-1.5 0.3B 83.39 0.097 76.25 83.65 0.090
MinerU2-VLM 0.9B 83.60 0.094 79.76 80.44 0.091
MonkeyOCR-pro-1.2B 1.9B 84.64 0.123 84.17 82.13 0.145
MonkeyOCR-3B 3.7B 84.65 0.100 84.16 79.81 0.143
Nanonets-OCR-s 3B 85.52 0.106 88.09 79.11 0.106
Deepseek-OCR 3B 86.17 0.078 83.59 82.69 0.085
dots.ocr 3B 86.87 0.083 83.27 85.68 0.081
MonkeyOCR-pro-3B 3.7B 86.94 0.103 86.29 84.86 0.141
MinerU2.5 1.2B 90.06 0.052 88.22 87.16 0.050
PaddleOCR-VL 0.9B 92.11 0.039 90.35 89.90 0.048
PaddleOCR-VL-1.5 0.9B 93.43 0.037 93.04 90.97 0.045
*In the scanning scenario, the models’ overall performance remains high, with PaddleOCR-VL-1.5 typically achieving the best results.* --- ### 2. Warping
Model Type Methods Parameters Overall↑ TextEdit↓ FormulaCDM↑ TableTEDS↑ Reading OrderEdit↓
Pipeline Tools Maker-1.8.2 - 58.98 0.349 72.71 39.08 0.390
PP-StructureV3 - 59.34 0.376 68.22 47.40 0.261
General VLMs GPT-5.2 - 76.26 0.239 80.90 71.80 0.165
Gemini-2.5 Pro - 87.63 0.092 86.50 85.59 0.109
Qwen2.5-VL-72B 72B 87.77 0.086 88.85 83.06 0.102
Gemini-3 Pro - 88.90 0.086 88.10 87.20 0.087
Qwen3-VL-235B-A22B-Instruct 235B 89.99 0.051 89.06 85.95 0.064
Specialized VLMs Dolphin-1.5 0.3B 50.50 0.383 47.24 42.52 0.309
Dolphin 322M 60.35 0.316 61.06 51.58 0.247
Deepseek-OCR 3B 67.20 0.328 73.59 60.80 0.226
MinerU2-VLM 0.9B 73.73 0.202 77.72 63.65 0.173
MonkeyOCR-pro-1.2B 1.9B 76.59 0.196 78.85 70.52 0.221
MonkeyOCR-3B 3.7B 77.27 0.164 79.08 69.18 0.211
MonkeyOCR-pro-3B 3.7B 78.90 0.168 79.55 73.94 0.212
Nanonets-OCR-s 3B 83.56 0.121 86.24 76.57 0.124
MinerU2.5 1.2B 83.76 0.154 85.92 80.71 0.104
PaddleOCR-VL 0.9B 85.97 0.093 85.45 81.77 0.092
dots.ocr 3B 86.01 0.087 85.03 81.74 0.093
PaddleOCR-VL-1.5 0.9B 91.25 0.053 90.94 88.10 0.063
*Warping documents introduce moderate challenges, causing a slight performance drop, but specialized VLMs maintain competitive accuracy.* --- ### 3. Screen-Photography
Model Type Methods Parameters Overall↑ TextEdit↓ FormulaCDM↑ TableTEDS↑ Reading OrderEdit↓
Pipeline Tools Maker-1.8.2 - 63.65 0.290 72.73 47.21 0.325
PP-StructureV3 - 66.89 0.204 73.26 47.82 0.165
General VLMs GPT-5.2 - 76.75 0.208 79.27 71.73 0.148
Qwen2.5-VL-72B 72B 86.48 0.100 87.46 82.00 0.102
Gemini-2.5 Pro - 87.11 0.103 85.30 86.31 0.117
Gemini-3 Pro - 88.86 0.084 87.33 87.65 0.087
Qwen3-VL-235B-A22B-Instruct 235B 89.27 0.068 88.72 85.85 0.071
Specialized VLMs Dolphin 322M 64.29 0.232 58.66 57.38 0.195
Dolphin-1.5 0.3B 69.76 0.205 61.80 68.00 0.177
Deepseek-OCR 3B 75.31 0.220 77.68 70.26 0.169
MinerU2-VLM 0.9B 78.77 0.139 79.02 71.17 0.123
MonkeyOCR-pro-1.2B 1.9B 80.24 0.148 80.78 74.74 0.179
MonkeyOCR-3B 3.7B 80.71 0.122 81.33 73.04 0.177
MonkeyOCR-pro-3B 3.7B 82.44 0.124 81.55 78.13 0.177
PaddleOCR-VL 0.9B 82.54 0.103 83.58 74.36 0.107
Nanonets-OCR-s 3B 84.86 0.112 86.65 79.09 0.117
dots.ocr 3B 87.18 0.081 85.34 84.26 0.079
MinerU2.5 1.2B 89.41 0.062 87.55 86.83 0.053
PaddleOCR-VL-1.5 0.9B 91.76 0.050 90.88 89.38 0.059
### 4. Illumination
Model Type Methods Parameters Overall↑ TextEdit↓ FormulaCDM↑ TableTEDS↑ Reading OrderEdit↓
Pipeline Tools Maker-1.8.2 - 66.31 0.259 74.80 50.03 0.337
PP-StructureV3 - 73.38 0.158 77.75 58.19 0.126
General VLMs GPT-5.2 - 80.88 0.191 84.41 77.37 0.134
Qwen2.5-VL-72B 72B 87.25 0.087 86.44 84.03 0.097
Gemini-2.5 Pro - 87.97 0.083 86.13 86.11 0.103
Qwen3-VL-235B-A22B-Instruct 235B 89.27 0.060 87.81 86.05 0.070
Gemini-3 Pro - 89.53 0.073 87.78 88.14 0.080
Specialized VLMs Dolphin 322M 67.29 0.197 61.42 60.10 0.173
Dolphin-1.5 0.3B 75.61 0.159 70.04 72.69 0.133
Deepseek-OCR 3B 78.10 0.192 81.71 71.81 0.156
MinerU2-VLM 0.9B 80.51 0.135 80.72 74.29 0.123
MonkeyOCR-pro-1.2B 1.9B 82.11 0.144 82.07 78.67 0.172
MonkeyOCR-3B 3.7B 83.16 0.118 83.63 77.62 0.168
MonkeyOCR-pro-3B 3.7B 84.71 0.120 84.13 82.02 0.171
Nanonets-OCR-s 3B 85.01 0.099 87.94 76.96 0.112
dots.ocr 3B 87.57 0.068 85.07 84.44 0.076
MinerU2.5 1.2B 89.57 0.065 88.36 86.87 0.062
PaddleOCR-VL 0.9B 89.61 0.049 86.66 87.02 0.055
PaddleOCR-VL-1.5 0.9B 92.16 0.046 91.80 89.33 0.051
*Severe illumination variations present significant challenges, resulting in a noticeable performance drop for most models.* --- ### 5. Skew
Model Type Methods Parameters Overall↑ TextEdit↓ FormulaCDM↑ TableTEDS↑ Reading OrderEdit↓
Pipeline Tools PP-StructureV3 - 37.98 0.557 44.37 25.27 0.417
Maker-1.8.2 - 41.27 0.536 60.16 17.23 0.543
General VLMs GPT-5.2 - 75.00 0.257 80.27 70.47 0.167
Qwen3-VL-235B-A22B-Instruct 235B 86.56 0.077 83.96 83.41 0.091
Qwen2.5-VL-72B 72B 86.90 0.077 87.26 81.14 0.091
Gemini-2.5 Pro - 89.07 0.077 87.89 86.99 0.104
Gemini-3 Pro - 89.45 0.080 88.33 88.06 0.092
Specialized VLMs Dolphin-1.5 0.3B 28.16 0.553 25.60 14.18 0.419
Dolphin 322M 44.83 0.500 51.34 33.22 0.321
MonkeyOCR-pro-1.2B 1.9B 62.18 0.292 66.25 49.46 0.317
Deepseek-OCR 3B 63.01 0.327 73.27 48.48 0.231
MonkeyOCR-pro-3B 3.7B 64.47 0.251 69.06 49.42 0.301
MonkeyOCR-3B 3.7B 65.67 0.248 69.23 52.59 0.300
MinerU2-VLM 0.9B 68.16 0.230 74.45 53.07 0.191
MinerU2.5 1.2B 75.24 0.305 81.78 74.39 0.151
PaddleOCR-VL 0.9B 77.47 0.192 78.81 72.83 0.193
Nanonets-OCR-s 3B 81.98 0.121 85.78 72.22 0.133
dots.ocr 3B 84.27 0.087 85.73 75.74 0.094
PaddleOCR-VL-1.5 0.9B 91.66 0.047 91.00 88.69 0.061
*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.