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.

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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.

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Dataset Structure

The dataset follows the directory structure shown below:

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.

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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.

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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.

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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.

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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.

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Acknowledgements

Real5-OmniDocBench is based on the original OmniDocBench dataset and adopts its evaluation protocols. We thank the authors of OmniDocBench for their foundational work.

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Citation

If you use Real5-OmniDocBench in your research, please cite our dataset and refer also to the original OmniDocBench paper.