GutenOCR: A Grounded Vision-Language Front-End for Documents
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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Hunter Heidenreich
claimed_verified
2026-01-22T17:12:09.233Z
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/avatars/dab7b28734b17a2ba66fd4ae40af000e.svg
Hunter Heidenreich
hheiden
user
hheiden
6971e218c1c7409747bf96fb
Ben Elliott
claimed_verified
2026-01-22T17:11:27.411Z
6970fcdb686be84c0f5a295a
/avatars/c34ccbc616ba2433836f04457e936f32.svg
Ben Elliott
bene-roots
user
bene-roots
6971e218c1c7409747bf96fc
Olivia Dinica
6971e218c1c7409747bf96fd
Yosheb Getachew
claimed_verified
2026-01-23T09:38:52.191Z
6970fd17a4ce432aba955aee
/avatars/1cfed682737cb4da62a076df73b381e6.svg
Yosheb Getachew
yosh-roots
user
yosh-roots
GutenOCR: A Grounded Vision-Language Front-End for Documents
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 enhances vision-language models for document understanding by enabling unified reading, detection, and grounding through prompt-based interfaces trained on diverse document types.
vision-language models fine-tuning grounded OCR prompt-based interface document understanding OCR evaluation protocol page-level linearization text-detection recall