Технический отчет OvisOCR2

Аннотация

我们介绍OvisOCR2，这是一个拥有0.8B参数的文档解析模型。OvisOCR2被设计为端到端解析器：给定一个文档页面图像，它以自然阅读顺序生成Markdown表示，涵盖文本、公式、表格和视觉区域。我们构建了一个数据引擎，该引擎将过滤后的真实文档注释与合成页面相结合，后者的渲染图像和Markdown目标均源自同一HTML来源。训练方案包括监督微调、在具有多组件奖励设计的4B分支上的强化学习、对0.8B模型进行策略蒸馏以及模型融合。在OmniDocBench v1.6上，OvisOCR2达到了96.58的总体最优分数，使端到端模型首次登上此前由流水线方法主导的排行榜榜首，突显了端到端文档解析的潜力。在PureDocBench上，OvisOCR2也取得了最高Avg3分数75.06。除了这两个公开基准测试外，我们还在一个内部基准上对OvisOCR2进行了评估，该基准旨在覆盖更广泛的长尾和具有挑战性的场景。OvisOCR2在比较方法中取得了最佳总体性能，进一步证明了其泛化能力和鲁棒性。OvisOCR2可在https://huggingface.co/ATH-MaaS/OvisOCR2获取。

English

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.

Технический отчет OvisOCR2

OvisOCR2 Technical Report

Аннотация

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