OvisOCR2技術報告

摘要

我們介紹了 OvisOCR2，這是一個 0.8B 參數的文件解析模型。OvisOCR2 被設計為一個端到端的解析器：給定一份文件頁面圖像，它能夠生成符合自然閱讀順序的 Markdown 表示，涵蓋文字、公式、表格與視覺區塊。我們建立了一個數據引擎，結合了經過過濾的真實文件註釋，以及從相同 HTML 來源生成其渲染影像與 Markdown 目標的合成頁面。訓練流程包括監督式微調、在 4B 分支上進行具有多組件獎勵設計的強化學習、將策略在線蒸餾至 0.8B 模型，以及模型融合。在 OmniDocBench v1.6 上，OvisOCR2 以 96.58 的總體分數達到當前最佳表現，使端到端模型首次登上此前以管線方法主導的排行榜首位，突顯了端到端文件解析的潛力。在 PureDocBench 上，OvisOCR2 同樣以 75.06 的 Avg3 分數取得最高成績。除了這兩個公開基準之外，我們還在一個內部基準上評估 OvisOCR2，該基準涵蓋了更廣泛的長尾與具挑戰性場景。在各比較方法中，OvisOCR2 獲得最佳總體表現，進一步證明了其泛化能力與穩健性。OvisOCR2 可在 https://huggingface.co/ATH-MaaS/OvisOCR2 取得。

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.

OvisOCR2技術報告

OvisOCR2 Technical Report

摘要

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