LightOnOCR-2

About

What LightOnOCR-2 is

[](https://lighton.ai) [](https://www.linkedin.com/company/lighton/) [](https://x.com/LightOnIO) 📄 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 <$0.01 per 1,000 pages 🧠 End-to-End: Fully differentiable, no external OCR pipeline 🧾 Versatile: Handles tables, receipts, forms, multi-column layouts, and math notation 📍 Image detection: Predicts bounding boxes for embedded images (bbox variants) 📄 Paper | 📝 Blog Post | 🚀 Demo | 📊 Dataset | 📊 BBox Dataset | 📓 Finetuning Notebook | LightOn blog entry Model Variants Benchmarks 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 [](https://

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How language models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys lightonocr-2 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (lightonocr-2 below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"lightonocr-2","messages":[{"role":"user","content":"Hello"}]}'

Details

Languages, data & research

Papers

Licence

Open weights

Sources

Weights & code

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