GutenOCR：面向文档的接地气视觉语言前端

摘要

GutenOCR是基于Qwen2.5-VL-3B与Qwen2.5-VL-7B微调得到的系列端到端OCR前端模型。这些单检查点的视觉语言模型通过统一的提示式接口，实现了文本识别、检测与定位功能。该模型基于商业文档、科学文献及合成定位数据训练，支持整页与局部阅读，可输出行级/段落级边界框，并响应条件式"X在哪里？"的查询。我们提出了带定位功能的OCR评估方案，实验表明GutenOCR-7B在1.05万份留存的商业与科学文档上的综合定位OCR得分较其骨干网络Qwen2.5-VL-7B提升超一倍（0.40→0.82）。在Fox与OmniDocBench v1.5基准测试中，本方法显著提升了区域/行级OCR性能及文本检测召回率，但在页面级线性化、色彩引导OCR及公式密集版块处理方面存在权衡。

English

GutenOCR is a family of grounded OCR front-ends obtained by fine-tuning Qwen2.5-VL-3B and Qwen2.5-VL-7B. The resulting single-checkpoint vision-language models expose reading, detection, and grounding through a unified, prompt-based interface. Trained on business documents, scientific articles, and synthetic grounding data, the models support full-page and localized reading with line- and paragraph-level bounding boxes and conditional ``where is x?'' queries. We introduce a grounded OCR evaluation protocol and show that GutenOCR-7B more than doubles the composite grounded OCR score of its Qwen2.5-VL-7B backbone on 10.5K held-out business and scientific pages (0.40 to 0.82). On Fox and OmniDocBench v1.5, our approach substantially improves region- and line-level OCR as well as text-detection recall, but reveals trade-offs in page-level linearization, color-guided OCR, and formula-heavy layouts.

GutenOCR：面向文档的接地气视觉语言前端

GutenOCR: A Grounded Vision-Language Front-End for Documents

摘要

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