# OvisOCR2 Technical Report

Page: https://stenobird.com/podcast/daily-paper-cast-7079649/ovisocr2-technical-report
Text version: https://stenobird.com/podcast/daily-paper-cast-7079649/ovisocr2-technical-report.md
Podcast: [Daily Paper Cast](https://stenobird.com/podcast/daily-paper-cast-7079649)
Published: 2026-07-17T03:46:32+00:00
Episode link: https://share.transistor.fm/s/49b396cf
Audio file: https://media.transistor.fm/49b396cf/f4474c79.mp3
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JSON: https://stenobird.com/v1/public/podcasts/daily-paper-cast-7079649/episodes/ovisocr2-technical-report
Duration seconds: 1246

## Resource

🤗 Upvotes: 43 | cs.CV, cs.AI Authors: Shiyin Lu, Yinglun Li, Yu Xia, Yuhui Chen, An-Yang Ji, Jun-Peng Jiang, Qing-Guo Chen, Jianshan Zhao, En Lin, Haijun Li, Cheng Qin, Zhao Xu, Weihua Luo Title: OvisOCR2 Technical Report Arxiv: http://arxiv.org/abs/2607.13639v1 Abstract: We introduce OvisOCR2, a 0.8B document parsing model. OvisOCR2 is designed as an end-to-end parser: given a document page image, it generates a Markdown representation in natural reading order, covering text, formulas, tables, and visual regions. We build a data engine that combines filtered real-document annotations with synthetic pages whose rendered images and Markdown targets are derived from the same HTML source. The training recipe includes supervised fine-tuning, reinforcement learning on a 4B branch with a multi-component reward design, on-policy distillation into the 0.8B model, and model fusion. On OmniDocBench v1.6, OvisOCR2 achieves a state-of-the-art overall score of 96.58, placing an end-to-end model at the top of this leaderboard previously dominated by pipeline methods and highlighting the potential of end-to-end document parsing. On PureDocBench, OvisOCR2 also achieves the highest Avg3 score of 75.06. Beyond these two public benchmarks, we evaluate OvisOCR2 on an in-house benchmark designed to cover a broader set of long-tail and challenging scenarios. OvisOCR2 obtains the best overall performance among the compared methods, providing further evidence of its generalization and robustness. OvisOCR2 is available at https://huggingface.co/ATH-MaaS/OvisOCR2.

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