TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents

🔥 News

2026/09/10 - We have renamed NaviDC-OCR to TeleOCR, and all subsequent model iterations will be developed and released under the TeleOCR version.
2026/09/01 - We noticed that EMNLP 2026 is hosting the Dr.DocBench Challenge, a document parsing competition. We evaluated NaviDC-OCR with its native weights, achieving better results than MinerU 2.5 Pro and PaddleOCR-VL 1.6. Detailed results are shown below dr.docbench-challenge. We welcome the use of NaviDC‑OCR for competitions. Going forward, we will continue to deliver competitive parsing models for the community.
2026/08/29 — Thanks to Nandraj for the GGUF conversion and llama.cpp support 🔗 NaviDC-OCR-GGUF, and to the community for sharing their experience deploying NaviDC-OCR on Ascend 910B!
2026/08/17 — NaviDC-OCR model weights and technical report have been released.

📖 Introduction

TeleOCR is a lightweight (~1.2B parameters), open-source Vision-Language Model designed specifically for document parsing.

Unlike existing methods that mainly target either digital documents or camera-captured documents, TeleOCR unifies both scenarios within a single framework.

Compared with previous document parsing models, TeleOCR introduces

Multi-node Consensus Voting (MCV) for automatic pseudo-label generation
Geometry-aware document modeling for camera-captured documents
Curvature-Guided Douglas-Peucker Sampling (CGDP)
Image-to-image self-verification for automatic data refinement
Progressive four-stage training pipeline
Content-Structure Decoupled Learning for tables and formulas

These techniques enable TeleOCR to achieve state-of-the-art performance on both digital and camera-captured document benchmarks while remaining lightweight enough for practical deployment.

📊 Experimental Results

TeleOCR achieves state-of-the-art performance on multiple public document parsing benchmarks.

Layout Visualization of Distorted Documents

To evaluate the model's ability to understand complex document deformations, we conduct a visual evaluation on the public dewarping datasets DocUNet and DIR300, with representative results shown in Figure. TeleOCR directly performs layout and content parsing on distorted documents without dewarping preprocessing or a dedicated rectification model, demonstrating robust parsing under complex geometric deformations.

Dr.DocBench Challenge

模型	overall ↑	Text edit ↓	formula cdm ↑	Table teds ↑	order edit ↓
Specialized VLMs
TeleOCR	67.96	0.1903	0.02	64.97	0.398
Mineru 2.5 pro	62.26	0.3402	0.04	67.75	0.356
OvisOCR2	59.25	0.3883	0.00	61.59	0.3791
PaddleOCRvl 1.6	55.11	0.4364	0.21	51.34	0.412

OmniDocBench v1.6

Model Type	Methods	Param	Overall ↑	Text Edit ↓	Formula CDM ↑	Table TEDS ↑	Table TEDS-S ↑	Read Order Edit ↓
Specialized VLMs	TeleOCR	1.2B	96.87	0.027	96.36	97.05	98.52	0.122
	OvisOCR2	0.8B	96.58	0.025	97.53	94.76	97.16	0.111
	PaddleOCR-VL-1.6	0.9B	96.33	0.033	97.49	94.76	97.11	0.127
	MinerU2.5-Pro	1.2B	95.75	0.036	97.45	93.42	95.92	0.120
	GLM-OCR	0.9B	95.22	0.044	97.18	92.83	95.39	0.133
	PaddleOCR-VL-1.5	0.9B	94.87	0.038	96.69	91.67	94.37	0.130
	HunyuanOCR-1.5	1B	94.74	0.033	97.49	94.76	97.11	0.127
	PaddleOCR-VL	0.9B	94.11	0.040	95.70	90.65	93.74	0.135
	Youtu-Parsing	2.5B	93.68	0.044	93.45	92.02	95.00	0.116
	Logics-Parsing-v2	4B	93.27	0.041	95.47	88.42	91.98	0.137
	FireRed-OCR	2B	93.20	0.037	95.27	88.04	91.06	0.131
	MinerU2.5	1.2B	92.98	0.045	95.59	87.88	91.47	0.130
	OpenDoc-0.1B	0.1B	90.64	0.049	92.93	83.88	87.45	0.140
	dots.ocr	3B	90.50	0.048	89.12	87.18	90.58	0.138
	DeepSeek-OCR 2	3B	90.17	0.050	91.59	83.89	87.75	0.144
	HunyuanOCR	1B	89.87	0.089	87.44	91.01	93.23	0.171
	Dolphin-v2	3B	89.34	0.069	90.53	84.40	87.44	0.150
	OCRVerse	4B	88.44	0.063	89.14	82.44	86.27	0.163
	MonkeyOCR-pro-3B	3B	88.43	0.074	88.33	84.35	88.62	0.189
General VLMs	Ovis2.6-30B-A3B	30B	93.62	0.035	94.93	89.44	92.40	0.135
	Gemini 3 Pro	--	92.85	0.064	95.83	89.15	92.96	0.165
	Gemini 3 Flash	--	92.58	0.066	95.03	89.29	93.51	0.173
	Qwen3-VL-235B	235B	89.78	0.063	92.53	83.07	86.75	0.166
	GPT-5.2	--	86.52	0.114	88.00	82.95	87.93	0.193
	InternVL3.5-241B	241B	83.61	0.130	89.52	74.35	79.78	0.215

Wild_OmniDocBench

Model Type	Methods	Param	Overall ↑	Text Edit ↓	Formula CDM ↑	Table TEDS ↑	Table TEDS-S ↑	Read Order Edit ↓
Decoupled VLMs	TeleOCR	1.2B	88.53	0.1173	88.26	89.05	92.14	0.2011
	PaddleOCR-VL-1.6	0.9B	87.36	0.1369	88.42	85.76	90.14	0.2057
	MinerU2.5-Pro	1.2B	87.33	0.1362	90.15	85.46	90.12	0.2013
	GLM-OCR	0.9B	85.08	0.1514	89.09	81.31	85.90	0.2228
	PaddleOCR-VL-1.5	0.9B	84.64	0.1461	86.72	81.80	86.52	0.2138
End-to-End VLMs	OvisOCR2	0.8B	87.91	0.129	90.37	85.13	89.11	0.2021
	dots.ocr	3B	81.84	0.1483	85.0	75.32	80.20	0.2200
	HunyuanOCR-1.5	1B	77.62	0.1979	85.12	67.54	70.67	0.2750
	Logics-Parsing-v2	4B	77.10	0.4029	91.4	80.19	87.16	0.2355

PureDocBench

Model Type	Model	Clean Overall ↑	Clean Text ↓	Clean Formula ↑	Clean Table ↑	Digital Degraded Overall ↑	Digital Degraded Text ↓	Digital Degraded Formula ↑	Digital Degraded Table ↑	Real Degraded Overall ↑	Real Degraded Text ↓	Real Degraded Formula ↑	Real Degraded Table ↑
Decoupled VLM	TeleOCR	86.90	0.111	81.01	91.09	77.47	0.206	72.59	80.45	70.85	0.302	65.11	77.66
	DotsMOCR	76.27	0.151	66.23	77.65	73.16	0.198	64.32	74.95	61.73	0.312	54.39	61.97
	MinerU2.5-Pro	75.87	0.222	65.14	84.68	71.77	0.272	61.79	80.73	62.56	0.375	52.70	72.47
	YouTu-Parsing	75.02	0.230	67.34	80.74	69.66	0.270	61.44	74.49	60.29	0.360	52.20	64.69
	PaddleOCR-VL-1.5	73.01	0.266	63.53	82.12	66.73	0.339	58.03	76.07	60.50	0.398	54.00	67.33
	GLM-OCR	68.65	0.314	57.89	79.44	63.06	0.383	53.23	74.21	58.31	0.433	50.34	67.83
	Dolphin-v2	65.90	0.342	59.80	72.12	60.24	0.393	52.20	67.86	44.92	0.553	39.98	50.04
	MonkeyOCR-pro-3B	62.23	0.346	48.46	72.83	57.40	0.397	45.57	66.32	46.49	0.511	38.18	52.43
End-to-End VLM	OvisOCR2	82.14	0.149	71.29	90.12	77.77	0.192	67.87	84.71	66.61	0.316	57.64	73.79
	FD-RL	78.38	0.193	68.21	86.22	76.33	0.214	67.16	83.22	67.04	0.298	58.82	72.08
	Logics-Parsing-v2	76.35	0.213	67.67	82.67	73.85	0.248	67.33	79.02	67.64	0.304	61.65	71.64
	dots.ocr	72.01	0.248	61.37	79.51	65.95	0.307	56.67	71.86	55.68	0.403	47.70	59.63
	Qianfan-OCR	57.22	0.370	49.79	58.83	50.85	0.438	44.41	51.96	45.06	0.494	39.08	45.53
General VLMs	Qwen3-VL-8B	72.44	0.261	65.10	78.35	72.03	0.266	64.88	77.82	62.73	0.342	55.55	66.81
	Kimi K2.6	72.32	0.303	66.93	80.30	69.95	0.322	64.69	77.31	68.02	0.335	62.44	75.14
	Gemini-3.1-Pro	70.04	0.306	65.63	75.08	69.28	0.322	65.81	74.24	71.98	0.300	68.62	77.26
	Qwen3.5-397B-A17B	69.12	0.233	65.26	65.40	68.34	0.244	63.91	65.53	62.70	0.287	60.70	56.12

ICDAR2026 Sci-ImageMiner

#	Team	RMS	TEDS	Weighted
1	TeleOCR	17.23	66.39	41.81
2	VLMinators	17.29	64.31	40.80
3	Ricoh_SRCB	16.23	61.12	38.67
4	Vassilis Sioros	14.94	55.20	35.07
5	DocMiner	12.67	53.72	33.19
6	Qwen3 VL 8B	14.08	57.86	35.97

If you would like to perform complete document parsing, please refer to our GitHub repository: https://github.com/caipeng328/NaviDC-OCR.

Citation

@article{teleocr,
  title={TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents},
  author={Cai, Peng and Zou, Zhaofan and Liu, Shifa and Wang, Yikun and Tang, Jiawei and Yang, Kaicheng and Tong, Meng and He, Zhongjiang and Sun, Hao},
  journal={arXiv preprint arXiv:2608.12898},
  year={2026}
}

Community Contributions

Thanks to Nandraj for the GGUF conversion and llama.cpp support! 🔗 NaviDC-OCR-GGUF

Acknowledgements

TeleOCR is built upon

MinerU
Qwen2.5-VL
Qwen3
Transformers
PyTorch
FlashAttention

We sincerely thank these excellent open-source projects.

Contact

If you have any questions, feel free to open an issue or contact us.

StarDoc-AI/TeleOCR