GutenOCR: A Grounded Vision-Language Front-End for Documents
This work addresses document OCR for business and scientific applications, but it is incremental as it builds on existing vision-language models with fine-tuning.
The paper tackled the problem of improving OCR for documents by developing GutenOCR, a family of vision-language models fine-tuned from Qwen2.5-VL backbones, which more than doubled the composite grounded OCR score from 0.40 to 0.82 on 10.5K held-out pages.
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.