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arXiv · 2601.14490

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

Hunter Heidenreich·Ben Elliott·Olivia Dinica·Yosheb Getachew
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Abstract

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.

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BibTeXRIS

Hunter Heidenreich, Ben Elliott, Olivia Dinica, Yosheb Getachew. 2026-01-22. GutenOCR: A Grounded Vision-Language Front-End for Documents. https://arxiv.org/abs/2601.14490

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