--- title: TeleOCR canonical_url: "https://www.modelscope.cn/models/XingChen-AGI/TeleOCR" md_url: "https://www.modelscope.cn/models/XingChen-AGI/TeleOCR.md" repository: XingChen-AGI/TeleOCR last_updated: 2026-09-23 pipeline_tag: image-text-to-text tasks: - image-text-to-text model_type: - qwen2_5_vl architectures: - Qwen2_5_VLForConditionalGeneration parameters: 1.4B tensor_type: - BF16 library_name: - safetensors language: - zh - en - ja downloads: 141 stars: 20 tags: - document-parsing - ocr - multimodal --- # TeleOCR > TeleOCR - XingChen-AGI 在 ModelScope 开源的模型。TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents - **Repository**: XingChen-AGI/TeleOCR - **Tasks**: image-text-to-text - **Parameters**: 1.4B - **Tags**: document-parsing, ocr, multimodal - **Downloads**: 141 - **Stars**: 20 - **Last updated**: 2026-09-23 Source: https://www.modelscope.cn/models/XingChen-AGI/TeleOCR ---

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

[![GitHub](https://img.shields.io/badge/GitHub-Repository-black?logo=github)](https://github.com/caipeng328/TeleOCR) [![Papers with Code: SOTA on OmniDocBench v1.6](https://paperswithcode.co/api/v1/papers/2608.12898/leaderboard-badge.svg?eval=25856&live=1)](https://paperswithcode.co/api/v1/papers/2608.12898/leaderboard-badge-link?eval=25856) [![TeleAI](https://img.shields.io/badge/webapp_on_teleai.com-blue?logo=data:image/png;base64,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&labelColor=white)](https://www.teleai.com.cn/docparse/DocumentParsing) [![arXiv](https://img.shields.io/badge/TeleOCR-Technical%20Report-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2608.12898)
## 🔥 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](https://eval.ai/web/challenges/challenge-page/2717/overview), 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](https://huggingface.co/nandraj/NaviDC-OCR-GGUF), and to the community for sharing their experience deploying NaviDC-OCR on [Ascend 910B!](https://zhuanlan.zhihu.com/p/2078516899093155893) ## 📖 Introduction **TeleOCR** is a lightweight, 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.
Parsing evaluation on the DIR300 dataset. Parsing evaluation on the DocUNet dataset.
--- ## 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](https://github.com/opendatalab/OmniDocBench) | 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](https://github.com/VirtualLUOUCAS/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](https://github.com/zhihengli-casia/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](https://sites.google.com/view/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 | --- ## 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.