LightOnOCR-2-1B

📄 Paper | 📝 Blog | 🚀 Demo | 📊 Dataset | 📓 Finetuning LightOnOCR-2-1B Best OCR model .** LightOnOCR-2-1B is **LightOn's** flagship OCR model, refined with RLVR training for maximum accuracy. We recommend this variant for most OCR tasks. About LightOnOCR-2 LightOnOCR-2 is an efficient end-to-end 1B-parameter vision-language model for converting documents (PDFs, scans, images) into clean, naturally ordered text without relying on brittle pipelines. This second version is trained on a larger and higher-quality corpus with stronger French, arXiv, and scan coverage, improved LaTeX handling, and cleaner normalization. LightOnOCR-2 achieves state-of-the-art performance on OlmOCR-Bench while being ~9× smaller and significantly faster than competing approaches. Highlights ⚡ **Speed:** 3.3× faster than Chandra OCR, 1.7× faster than OlmOCR, 5× faster than dots.ocr, 2× faster than PaddleOCR-VL-0.9B, 1.73× faster than DeepSeekOCR 💸 **Efficiency:** Processes 5.71 pages/s on a single H100 (~493k pages/day) for ** See the paper for full benchmark details and methodology.* Usage with Transformers **Note:** LightOnOCR-2 is avaible in latest transformers release starting from v5. Usage with vLLM Rendering and Preprocessing Tips Render PDFs at 200 DPI to images using a target longest dimension of **1540px** Maintain aspect ratio to preserve text geometry Fine-tuning LightOnOCR-2 is fully differentiable and supports: LoRA fine-tuning Domain adaptation (receipts, scientific articles, forms, etc.) Multilingual fine-tuning with task-specific corpora For fine-tuning, we recommend starting with the **LightOnOCR-2-1B-base** variant. License Apache License 2.0 Acknowlegments The project received funding from the BPI Scribe project. Citation