lightonai / LightOnOCR-1B-1025

LightOnOCR-1B-1025

Full BF16 version of the model. We recommend this variant for further fine-tuning or research use.

LightOnOCR-1B is a compact, end-to-end vision–language model for Optical Character Recognition (OCR) and document understanding. It achieves state-of-the-art accuracy in its weight class while being several times faster and cheaper than larger general-purpose VLMs.

📝 Read the full blog post | 🚀 Try the demo

Highlights

• ⚡ Speed: 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
• 🌍 Compact variants: 32k and 16k vocab options for European languages

Model Overview

LightOnOCR combines a high-performance Vision Transformer encoder with a lightweight text decoder distilled from high-quality open VLMs. It is optimized for document parsing tasks, producing accurate, layout-aware text extraction from high-resolution pages.

Benchmarks

All benchmarks evaluated using standardized LightOnOCR inference via vLLM on the LightOn internal OCR test suite (2025-10).

Installation

uv venv --python 3.12 --seed
source .venv/bin/activate

uv pip install -U vllm \
    --torch-backend=auto \
    --extra-index-url https://wheels.vllm.ai/nightly \
    --prerelease=allow

uv pip install pypdfium2 pillow requests

Start Server

vllm serve lightonai/LightOnOCR-1B-1025 \
    --limit-mm-per-prompt '{"image": 1}' \
    --async-scheduling

PDF Inference

import base64
import requests
import pypdfium2 as pdfium
import io

ENDPOINT = "http://localhost:8000/v1/chat/completions"
MODEL = "lightonai/LightOnOCR-1B-1025"

# Download PDF from arXiv
pdf_url = "https://arxiv.org/pdf/2412.13663"
pdf_data = requests.get(pdf_url).content

# Open PDF and convert first page to image
pdf = pdfium.PdfDocument(pdf_data)
page = pdf[0]
# Render at 300 DPI (scale factor = 300/72 ≈ 4.17)
pil_image = page.render(scale=4.17).to_pil()

# Convert to base64
buffer = io.BytesIO()
pil_image.save(buffer, format="PNG")
image_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8')

# Make request
payload = {
    "model": MODEL,
    "messages": [{
        "role": "user",
        "content": [{
            "type": "image_url",
            "image_url": {"url": f"data:image/png;base64,{image_base64}"}
        }]
    }],
    "max_tokens": 6500,
    "temperature": 0.2,
    "top_p": 0.9,
}

response = requests.post(ENDPOINT, json=payload)
text = response.json()['choices'][0]['message']['content']
print(text)

Rendering and Preprocessing Tips

• Render PDFs to PNG or JPEG at a target longest dimension of 1280–1300 px
• Maintain aspect ratio to preserve text geometry
• LightOnOCR is robust to moderate skew; heavy rotation correction is optional
• Use one image per page; batching supported by vLLM

Variants

Fine-tuning

Transformers integration is coming soon for training.

LightOnOCR is fully differentiable and supports:

• LoRA fine-tuning
• Domain adaptation (receipts, scientific articles, forms, etc.)
• Multilingual fine-tuning with task-specific corpora

Example fine-tuning configurations will be released alongside the dataset.

Data

Trained on a diverse large-scale PDF corpus covering:

• Scientific papers, books, receipts, invoices, tables, forms, and handwritten text
• Multiple languages (Latin alphabet dominant)
• Real and synthetic document scans

The dataset will be released under an open license.

License

Apache License 2.0

Citation

@misc{lightonocr2025,
  title        = {LightOnOCR-1B: End-to-End and Efficient Domain-Specific Vision-Language Models for OCR},
  author       = {Said Taghadouini and Baptiste Aubertin and Adrien Cavaillès},
  year         = {2025},
  howpublished = {\url{https://huggingface.co/blog/lightonai/lightonocr}}
}