Real5-OmniDocBench measures the robustness of document parsing systems under five physical acquisition conditions: Scanning, Warping, Screen-Photography, Illumination, and Skew. It reconstructs the same 1,355 pages from OmniDocBench v1.5 in every condition, producing 6,775 images in total. The one-to-one page correspondence and shared evaluation protocol isolate the effect of acquisition conditions from changes in document content.

News

• 2026-08-27: Added evaluation results for NaviDC-OCR.
• 2026-08-08: Added evaluation results for Kimi-K2.5、Kimi-K2.6、Doubao-Seed-2.1-Pro、MonkeyOCRv2-S-Parsing、MonkeyOCRv2-B-Parsing and OvisOCR2.
• 2026-06-18: Real5-OmniDocBench was accepted to ECCV 2026. 🎉

Earlier updates

• 2026-05-28: Added evaluation results for PaddleOCR-VL-1.6 and MinerU2.5-Pro.
• 2026-03-05: Released the paper and added results for DeepSeek-OCR 2 and GLM-OCR.
• 2026-01-28: Released the dataset and benchmark.

Leaderboard

The leaderboard reports performance over all five acquisition conditions. All metrics follow OmniDocBench: Overall↑, TextEdit↓, FormulaCDM↑, TableTEDS↑, and Reading OrderEdit↓.

Higher is better for ↑ metrics and lower is better for ↓ metrics. Best results in each column are shown in bold, and second-best results are underlined. Tables are sorted by Overall score in descending order.

1. Overall

Overall is the mean score across all five scenarios.

View per-scenario leaderboards

2. Scanning

Methods	Model Type	Parameters	Overall↑	TextEdit↓	FormulaCDM↑	TableTEDS↑	Reading OrderEdit↓
PaddleOCR-VL-1.6	Specialized VLMs	0.9B	94.74	0.035	93.65	94.12	0.042
OvisOCR2	Specialized VLMs	0.9B	93.77	0.041	92.13	93.30	0.039
PaddleOCR-VL-1.5	Specialized VLMs	0.9B	93.43	0.037	93.04	90.97	0.045
GLM-OCR	Specialized VLMs	0.9B	92.67	0.054	91.10	92.28	0.061
NaviDC-OCR	Specialized VLMs	1.2B	92.33	0.045	88.26	93.26	0.039
PaddleOCR-VL	Specialized VLMs	0.9B	92.11	0.039	90.35	89.90	0.048
MinerU2.5-pro	Specialized VLMs	1.2B	92.11	0.040	89.77	90.57	0.043
Kimi-K2.6	General VLMs	1.1T	90.08	0.062	89.02	85.77	0.072
MinerU2.5	Specialized VLMs	1.2B	90.06	0.052	88.22	87.16	0.050
Kimi-K2.5	General VLMs	1.1T	89.67	0.060	88.67	86.31	0.079
Deepseek-OCR 2	Specialized VLMs	3B	89.59	0.055	88.55	85.72	0.056
MonkeyOCRv2-B-Parsing	Specialized VLMs	0.7B	89.49	0.053	86.29	87.53	0.051
Gemini-3 Pro	General VLMs	-	89.47	0.071	88.16	87.37	0.078
Qwen3-VL-235B	General VLMs	235B	89.43	0.059	89.01	85.19	0.066
Gemini-2.5 Pro	General VLMs	-	89.25	0.073	87.44	87.62	0.098
MonkeyOCRv2-S-Parsing	Specialized VLMs	0.6B	88.87	0.058	86.32	86.09	0.053
Doubao-Seed-2.1-Pro	General VLMs	-	88.85	0.084	86.56	88.33	0.093
MonkeyOCR-pro-3B	Specialized VLMs	3.7B	86.94	0.103	86.29	84.86	0.141
dots.ocr	Specialized VLMs	3B	86.87	0.083	83.27	85.68	0.081
Qwen2.5-VL-72B	General VLMs	72B	86.19	0.110	86.14	83.41	0.114
Deepseek-OCR	Specialized VLMs	3B	86.17	0.078	83.59	82.69	0.085
Nanonets-OCR-s	Specialized VLMs	3B	85.52	0.106	88.09	79.11	0.106
PP-StructureV3	Pipeline Tools	-	84.68	0.094	84.34	79.06	0.092
MonkeyOCR-3B	Specialized VLMs	3.7B	84.65	0.100	84.16	79.81	0.143
MonkeyOCR-pro-1.2B	Specialized VLMs	1.9B	84.64	0.123	84.17	82.13	0.145
GPT-5.2	General VLMs	-	84.43	0.142	85.68	81.78	0.109
MinerU2-VLM	Specialized VLMs	0.9B	83.60	0.094	79.76	80.44	0.091
Dolphin-1.5	Specialized VLMs	0.3B	83.39	0.097	76.25	83.65	0.090
Dolphin	Specialized VLMs	322M	72.16	0.154	64.58	67.27	0.130
Marker-1.8.2	Pipeline Tools	-	70.27	0.223	77.03	56.05	0.238

---

3. Warping

Methods	Model Type	Parameters	Overall↑	TextEdit↓	FormulaCDM↑	TableTEDS↑	Reading OrderEdit↓
PaddleOCR-VL-1.6	Specialized VLMs	0.9B	92.48	0.048	91.63	90.66	0.061
OvisOCR2	Specialized VLMs	0.9B	91.40	0.058	90.94	89.08	0.058
PaddleOCR-VL-1.5	Specialized VLMs	0.9B	91.25	0.053	90.94	88.10	0.063
GLM-OCR	Specialized VLMs	0.9B	90.68	0.071	90.30	88.78	0.100
NaviDC-OCR	Specialized VLMs	1.2B	90.63	0.062	89.14	88.93	0.051
Qwen3-VL-235B	General VLMs	235B	89.99	0.051	89.06	85.95	0.064
MonkeyOCRv2-B-Parsing	Specialized VLMs	0.7B	89.70	0.058	87.50	87.39	0.052
Kimi-K2.6	General VLMs	1.1T	89.62	0.073	91.18	85.02	0.079
Doubao-Seed-2.1-Pro	General VLMs	-	89.36	0.087	87.74	89.03	0.095
Gemini-3 Pro	General VLMs	-	88.90	0.086	88.10	87.20	0.087
Kimi-K2.5	General VLMs	1.1T	88.86	0.069	89.77	83.71	0.084
MinerU2.5-pro	Specialized VLMs	1.2B	88.72	0.100	87.81	88.36	0.076
MonkeyOCRv2-S-Parsing	Specialized VLMs	0.6B	88.17	0.069	86.20	85.17	0.056
Qwen2.5-VL-72B	General VLMs	72B	87.77	0.086	88.85	83.06	0.102
Gemini-2.5 Pro	General VLMs	-	87.63	0.092	86.50	85.59	0.109
dots.ocr	Specialized VLMs	3B	86.01	0.087	85.03	81.74	0.093
PaddleOCR-VL	Specialized VLMs	0.9B	85.97	0.093	85.45	81.77	0.092
MinerU2.5	Specialized VLMs	1.2B	83.76	0.154	85.92	80.71	0.104
Nanonets-OCR-s	Specialized VLMs	3B	83.56	0.121	86.24	76.57	0.124
MonkeyOCR-pro-3B	Specialized VLMs	3.7B	78.90	0.168	79.55	73.94	0.212
MonkeyOCR-3B	Specialized VLMs	3.7B	77.27	0.164	79.08	69.18	0.211
MonkeyOCR-pro-1.2B	Specialized VLMs	1.9B	76.59	0.196	78.85	70.52	0.221
GPT-5.2	General VLMs	-	76.26	0.239	80.90	71.80	0.165
MinerU2-VLM	Specialized VLMs	0.9B	73.73	0.202	77.72	63.65	0.173
Deepseek-OCR	Specialized VLMs	3B	67.20	0.328	73.59	60.80	0.226
Deepseek-OCR 2	Specialized VLMs	3B	66.53	0.293	70.42	58.44	0.209
Dolphin	Specialized VLMs	322M	60.35	0.316	61.06	51.58	0.247
PP-StructureV3	Pipeline Tools	-	59.34	0.376	68.22	47.40	0.261
Marker-1.8.2	Pipeline Tools	-	58.98	0.349	72.71	39.08	0.390
Dolphin-1.5	Specialized VLMs	0.3B	50.50	0.383	47.24	42.52	0.309

---

4. Screen-Photography

Methods	Model Type	Parameters	Overall↑	TextEdit↓	FormulaCDM↑	TableTEDS↑	Reading OrderEdit↓
OvisOCR2	Specialized VLMs	0.9B	93.09	0.054	91.80	92.83	0.046
PaddleOCR-VL-1.6	Specialized VLMs	0.9B	92.78	0.045	90.64	92.19	0.054
PaddleOCR-VL-1.5	Specialized VLMs	0.9B	91.76	0.050	90.88	89.38	0.059
GLM-OCR	Specialized VLMs	0.9B	91.75	0.063	89.83	91.66	0.070
MinerU2.5-pro	Specialized VLMs	1.2B	91.29	0.050	87.41	91.44	0.044
Kimi-K2.6	General VLMs	1.1T	89.58	0.073	90.10	85.49	0.077
MinerU2.5	Specialized VLMs	1.2B	89.41	0.062	87.55	86.83	0.053
Qwen3-VL-235B	General VLMs	235B	89.27	0.068	88.72	85.85	0.071
NaviDC-OCR	Specialized VLMs	1.2B	89.09	0.078	85.80	89.30	0.057
Doubao-Seed-2.1-Pro	General VLMs	-	88.99	0.098	87.47	89.31	0.102
Gemini-3 Pro	General VLMs	-	88.86	0.084	87.33	87.65	0.087
MonkeyOCRv2-B-Parsing	Specialized VLMs	0.7B	88.40	0.070	87.07	85.12	0.050
Kimi-K2.5	General VLMs	1.1T	88.39	0.070	87.41	84.77	0.078
MonkeyOCRv2-S-Parsing	Specialized VLMs	0.6B	87.64	0.068	84.96	84.72	0.049
dots.ocr	Specialized VLMs	3B	87.18	0.081	85.34	84.26	0.079
Gemini-2.5 Pro	General VLMs	-	87.11	0.103	85.30	86.31	0.117
Qwen2.5-VL-72B	General VLMs	72B	86.48	0.100	87.46	82.00	0.102
Nanonets-OCR-s	Specialized VLMs	3B	84.86	0.112	86.65	79.09	0.117
PaddleOCR-VL	Specialized VLMs	0.9B	82.54	0.103	83.58	74.36	0.107
MonkeyOCR-pro-3B	Specialized VLMs	3.7B	82.44	0.124	81.55	78.13	0.177
MonkeyOCR-3B	Specialized VLMs	3.7B	80.71	0.122	81.33	73.04	0.177
MonkeyOCR-pro-1.2B	Specialized VLMs	1.9B	80.24	0.148	80.78	74.74	0.179
MinerU2-VLM	Specialized VLMs	0.9B	78.77	0.139	79.02	71.17	0.123
GPT-5.2	General VLMs	-	76.75	0.208	79.27	71.73	0.148
Deepseek-OCR	Specialized VLMs	3B	75.31	0.220	77.68	70.26	0.169
Deepseek-OCR 2	Specialized VLMs	3B	71.65	0.201	73.49	61.54	0.157
Dolphin-1.5	Specialized VLMs	0.3B	69.76	0.205	61.80	68.00	0.177
PP-StructureV3	Pipeline Tools	-	66.89	0.204	73.26	47.82	0.165
Dolphin	Specialized VLMs	322M	64.29	0.232	58.66	57.38	0.195
Marker-1.8.2	Pipeline Tools	-	63.65	0.290	72.73	47.21	0.325

---

5. Illumination

Methods	Model Type	Parameters	Overall↑	TextEdit↓	FormulaCDM↑	TableTEDS↑	Reading OrderEdit↓
PaddleOCR-VL-1.6	Specialized VLMs	0.9B	93.28	0.042	92.61	91.48	0.051
OvisOCR2	Specialized VLMs	0.9B	92.88	0.043	91.18	91.72	0.040
PaddleOCR-VL-1.5	Specialized VLMs	0.9B	92.16	0.046	91.80	89.33	0.051
MinerU2.5-pro	Specialized VLMs	1.2B	91.31	0.050	88.15	90.76	0.052
GLM-OCR	Specialized VLMs	0.9B	91.12	0.059	91.02	88.20	0.071
NaviDC-OCR	Specialized VLMs	1.2B	90.90	0.055	87.27	90.91	0.045
Kimi-K2.6	General VLMs	1.1T	89.91	0.062	89.76	86.20	0.072
Kimi-K2.5	General VLMs	1.1T	89.66	0.064	89.83	85.53	0.077
PaddleOCR-VL	Specialized VLMs	0.9B	89.61	0.049	86.66	87.02	0.055
MinerU2.5	Specialized VLMs	1.2B	89.57	0.065	88.36	86.87	0.062
Gemini-3 Pro	General VLMs	-	89.53	0.073	87.78	88.14	0.080
Qwen3-VL-235B	General VLMs	235B	89.27	0.060	87.81	86.05	0.070
Doubao-Seed-2.1-Pro	General VLMs	-	89.13	0.085	87.29	88.59	0.091
MonkeyOCRv2-B-Parsing	Specialized VLMs	0.7B	88.54	0.066	86.72	85.53	0.059
Gemini-2.5 Pro	General VLMs	-	87.97	0.083	86.13	86.11	0.103
dots.ocr	Specialized VLMs	3B	87.57	0.068	85.07	84.44	0.076
Qwen2.5-VL-72B	General VLMs	72B	87.25	0.087	86.44	84.03	0.097
MonkeyOCRv2-S-Parsing	Specialized VLMs	0.6B	86.75	0.074	84.16	83.49	0.057
Nanonets-OCR-s	Specialized VLMs	3B	85.01	0.099	87.94	76.96	0.112
MonkeyOCR-pro-3B	Specialized VLMs	3.7B	84.71	0.120	84.13	82.02	0.171
MonkeyOCR-3B	Specialized VLMs	3.7B	83.16	0.118	83.63	77.62	0.168
MonkeyOCR-pro-1.2B	Specialized VLMs	1.9B	82.11	0.144	82.07	78.67	0.172
GPT-5.2	General VLMs	-	80.88	0.191	84.41	77.37	0.134
MinerU2-VLM	Specialized VLMs	0.9B	80.51	0.135	80.72	74.29	0.123
Deepseek-OCR	Specialized VLMs	3B	78.10	0.192	81.71	71.81	0.156
Deepseek-OCR 2	Specialized VLMs	3B	76.02	0.168	77.83	67.01	0.122
Dolphin-1.5	Specialized VLMs	0.3B	75.61	0.159	70.04	72.69	0.133
PP-StructureV3	Pipeline Tools	-	73.38	0.158	77.75	58.19	0.126
Dolphin	Specialized VLMs	322M	67.29	0.197	61.42	60.10	0.173
Marker-1.8.2	Pipeline Tools	-	66.31	0.259	74.80	50.03	0.337

---

6. Skew

Methods	Model Type	Parameters	Overall↑	TextEdit↓	FormulaCDM↑	TableTEDS↑	Reading OrderEdit↓
PaddleOCR-VL-1.6	Specialized VLMs	0.9B	92.66	0.045	91.44	91.04	0.058
PaddleOCR-VL-1.5	Specialized VLMs	0.9B	91.66	0.047	91.00	88.69	0.061
NaviDC-OCR	Specialized VLMs	1.2B	90.63	0.054	87.15	90.10	0.042
OvisOCR2	Specialized VLMs	0.9B	90.33	0.048	90.58	85.23	0.048
MonkeyOCRv2-B-Parsing	Specialized VLMs	0.7B	89.97	0.051	88.10	86.88	0.051
Kimi-K2.6	General VLMs	1.1T	89.61	0.060	90.25	84.27	0.078
Gemini-3 Pro	General VLMs	-	89.45	0.080	88.33	88.06	0.092
Gemini-2.5 Pro	General VLMs	-	89.07	0.077	87.89	86.99	0.104
Kimi-K2.5	General VLMs	1.1T	88.86	0.060	89.62	83.00	0.080
Doubao-Seed-2.1-Pro	General VLMs	-	88.79	0.080	88.23	86.11	0.095
MonkeyOCRv2-S-Parsing	Specialized VLMs	0.6B	88.09	0.063	84.54	86.07	0.055
Qwen2.5-VL-72B	General VLMs	72B	86.90	0.077	87.26	81.14	0.091
Qwen3-VL-235B	General VLMs	235B	86.56	0.077	83.96	83.41	0.091
GLM-OCR	Specialized VLMs	0.9B	85.39	0.099	85.78	80.28	0.156
dots.ocr	Specialized VLMs	3B	84.27	0.087	85.73	75.74	0.094
Nanonets-OCR-s	Specialized VLMs	3B	81.98	0.121	85.78	72.22	0.133
MinerU2.5-pro	Specialized VLMs	1.2B	81.26	0.202	83.92	81.07	0.107
PaddleOCR-VL	Specialized VLMs	0.9B	77.47	0.192	78.81	72.83	0.193
MinerU2.5	Specialized VLMs	1.2B	75.24	0.305	81.78	74.39	0.151
GPT-5.2	General VLMs	-	75.00	0.257	80.27	70.47	0.167
MinerU2-VLM	Specialized VLMs	0.9B	68.16	0.230	74.45	53.07	0.191
MonkeyOCR-3B	Specialized VLMs	3.7B	65.67	0.248	69.23	52.59	0.300
MonkeyOCR-pro-3B	Specialized VLMs	3.7B	64.47	0.251	69.06	49.42	0.301
Deepseek-OCR	Specialized VLMs	3B	63.01	0.327	73.27	48.48	0.231
MonkeyOCR-pro-1.2B	Specialized VLMs	1.9B	62.18	0.292	66.25	49.46	0.317
Deepseek-OCR 2	Specialized VLMs	3B	61.28	0.295	66.16	47.18	0.221
Dolphin	Specialized VLMs	322M	44.83	0.500	51.34	33.22	0.321
Marker-1.8.2	Pipeline Tools	-	41.27	0.536	60.16	17.23	0.543
PP-StructureV3	Pipeline Tools	-	37.98	0.557	44.37	25.27	0.417
Dolphin-1.5	Specialized VLMs	0.3B	28.16	0.553	25.60	14.18	0.419

Benchmark Overview

Real5-OmniDocBench evaluates the same document content under controlled changes to the physical acquisition process. Except for the scanning subset, images were manually captured with handheld mobile devices.

This design provides:

• Controlled comparison: every scenario contains the same 1,355 source pages.
• Physical realism: acquisition artifacts are produced by real devices and environments rather than synthetic transformations.
• Protocol compatibility: page identities, annotations, prediction format, and metrics follow OmniDocBench v1.5.

Dataset

The five scenario directories each contain 1,355 images. The complete download is approximately 16 GB.

Real5-OmniDocBench/
├── Real5-OmniDocBench-Scanning/
├── Real5-OmniDocBench-Warping/
├── Real5-OmniDocBench-Screen-Photography/
├── Real5-OmniDocBench-Illumination/
└── Real5-OmniDocBench-Skew/

Install the current Hugging Face Hub CLI and download the complete dataset:

pip install -U huggingface_hub
hf download PaddlePaddle/Real5-OmniDocBench \
  --repo-type dataset \
  --local-dir ./Real5-OmniDocBench

To download a single condition, use a file pattern:

hf download PaddlePaddle/Real5-OmniDocBench \
  --repo-type dataset \
  --include "Real5-OmniDocBench-Warping/*" \
  --local-dir ./Real5-OmniDocBench

Download the matching OmniDocBench v1.5 ground-truth annotations separately:

hf download opendatalab/OmniDocBench OmniDocBench.json \
  --repo-type dataset \
  --revision v1_5 \
  --local-dir ./OmniDocBench-v1.5

Evaluation

Real5-OmniDocBench does not introduce a new prediction schema. It reuses the OmniDocBench v1.5 annotation format and evaluation pipeline so that performance can be compared across acquisition conditions.

1. Run the model independently on all 1,355 images in each scenario.
2. Export predictions in the OmniDocBench end-to-end parsing format.
3. Match each image to its corresponding OmniDocBench v1.5 ground-truth annotation.
4. Apply the same preprocessing, evaluator, and metric settings to every scenario.

Refer to the version-pinned OmniDocBench v1.5 evaluation guide for environment setup, prediction formats, and evaluation commands.

Metrics

Submit Results

Model results can appear in the Hugging Face Hub leaderboard through the Hub evaluation-results workflow. Add an evaluation result file under .eval_results/ in the model repository, set evaluation_framework: real5-omnidocbench, and follow the Hugging Face evaluation results documentation for the supported schema and submission process.

Citation

If you use Real5-OmniDocBench in your research, please cite the following paper. Please also cite OmniDocBench when using its annotations or evaluation pipeline.

@misc{zhou2026real5omnidocbench,
  title         = {Real5-OmniDocBench: A Full-Scale Physical Reconstruction Benchmark for Robust Document Parsing in the Wild},
  author        = {Changda Zhou and Ziyue Gao and Xueqing Wang and Tingquan Gao and Cheng Cui and Jing Tang and Yi Liu},
  year          = {2026},
  eprint        = {2603.04205},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  doi           = {10.48550/arXiv.2603.04205},
  url           = {https://arxiv.org/abs/2603.04205}
}

Acknowledgements

Real5-OmniDocBench is built on OmniDocBench v1.5 and adopts its annotations and evaluation protocol. We thank the OmniDocBench authors for making their benchmark and evaluation tools available to the community.

License

The Real5-OmniDocBench dataset and repository materials are released under the Apache License 2.0. This benchmark inherits annotations and source-page correspondence from OmniDocBench v1.5; use of those materials remains subject to the applicable OmniDocBench and source-document terms.