The Year in AI Papers
The year/Independent research

Paper 2607.13639

OvisOCR2 Technical Report

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, et al.

Published
Jul 2026
Research lab
Independent
Citations
0
GitHub
Not linked

01 In brief

Summary

OvisOCR2 is a 0.8B end-to-end document parsing model that converts document page images into Markdown, covering text, formulas, tables, and visual regions.

It uses a data engine combining filtered real-document annotations with synthetic pages generated from HTML sources.

Training includes supervised fine-tuning, reinforcement learning (GRPO) on a 4B branch with multi-component rewards (text edit distance, formula CDM, table TEDS), 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, surpassing pipeline methods like PaddleOCR-VL-1.6 and MinerU2.5-Pro.

On PureDocBench, it achieves the highest Avg3 score of 75.06.

On an in-house benchmark of over 1,000 pages, it leads overall and across difficulty tiers, including handwriting and complex-table subsets.

The model is available on Hugging Face.

02 From the paper

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.