0.8B小模型干翻大厂流水线，文档解析新王登基
文档解析（把PDF转成Markdown）一直是AI的苦活，过去得靠「版面检测→OCR→公式识别→表格重建」一堆模型串起来，又慢又容易断。现在一个0.8B参数的端到端模型OvisOCR2，直接一张图进去、Markdown出来，在OmniDocBench上拿下96.58分，把之前所有流水线方案踩在脚下。它怎么做到的？核心是数据引擎：用真实文档标注+从同一HTML渲染出图片和Markdown的合成数据，再用4B大模型做强化学习、蒸馏到0.8B小模型。结果就是：小模型、单步、还最强。它不是你能明天拿来用的工具（模型已开源），但信号很清楚：端到端正在吃掉文档解析这个赛道。
📄 原文摘要(英文)
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.