LightOnOCR: A 1B End-to-End Multilingual Vision-Language Model for State-of-the-Art OCR
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arXiv
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| Format: | Preprint |
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2026
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| author | Taghadouini, Said Cavaillès, Adrien Aubertin, Baptiste |
| author_facet | Taghadouini, Said Cavaillès, Adrien Aubertin, Baptiste |
| contents | We present \textbf{LightOnOCR-2-1B}, a 1B-parameter end-to-end multilingual vision--language model that converts document images (e.g., PDFs) into clean, naturally ordered text without brittle OCR pipelines. Trained on a large-scale, high-quality distillation mix with strong coverage of scans, French documents, and scientific PDFs, LightOnOCR-2 achieves state-of-the-art results on OlmOCR-Bench while being 9$\times$ smaller and substantially faster than prior best-performing models. We further extend the output format to predict normalized bounding boxes for embedded images, introducing localization during pretraining via a resume strategy and refining it with RLVR using IoU-based rewards. Finally, we improve robustness with checkpoint averaging and task-arithmetic merging. We release model checkpoints under Apache 2.0, and publicly release the dataset and \textbf{LightOnOCR-bbox-bench} evaluation under their respective licenses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_14251 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LightOnOCR: A 1B End-to-End Multilingual Vision-Language Model for State-of-the-Art OCR Taghadouini, Said Cavaillès, Adrien Aubertin, Baptiste Computer Vision and Pattern Recognition We present \textbf{LightOnOCR-2-1B}, a 1B-parameter end-to-end multilingual vision--language model that converts document images (e.g., PDFs) into clean, naturally ordered text without brittle OCR pipelines. Trained on a large-scale, high-quality distillation mix with strong coverage of scans, French documents, and scientific PDFs, LightOnOCR-2 achieves state-of-the-art results on OlmOCR-Bench while being 9$\times$ smaller and substantially faster than prior best-performing models. We further extend the output format to predict normalized bounding boxes for embedded images, introducing localization during pretraining via a resume strategy and refining it with RLVR using IoU-based rewards. Finally, we improve robustness with checkpoint averaging and task-arithmetic merging. We release model checkpoints under Apache 2.0, and publicly release the dataset and \textbf{LightOnOCR-bbox-bench} evaluation under their respective licenses. |
| title | LightOnOCR: A 1B End-to-End Multilingual Vision-Language Model for State-of-the-Art OCR |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.14251 |