{"repo":"stanford-oval/Churro","free":true,"listed":false,"github":"https://github.com/stanford-oval/Churro","clone":"git clone https://github.com/stanford-oval/Churro.git","description":"CHURRO is an OCR toolkit for historical document transcription, built to make handwritten and printed sources readable at high accuracy and lower cost.","language":"Python","stars":68,"topics":["ai","document-parser","document-parsing","documents","large-language-models","markdown","ocr","pdf","pdf-converter","pdf-to-markdown"],"license":"Apache-2.0","category":"media-processing","readme_excerpt":"Churro 🤗 Model • 🗂️ Dataset • 📄 Paper 📚 Docs • 🏆 Leaderboard • Churro is the fastest way to turn hard-to-read historical scans into reliable text. It gives researchers, libraries, archives, and product teams a unified OCR toolkit for handwritten and printed sources, combining high accuracy, low operating cost, and a clean Python API and CLI workflow. Supported OCR Models and Backends We provide first-party support for Churro VLM, the best OCR model for historical documents. Churro also includes built-in profiles, templates, and post-processing for many other models and integrations, including: - Hosted vision-language models, including Gemini, GPT, Claude, and more, through LiteLLM integration - OpenAI-compatible servers, including vLLM, Ollama, TGI, and more - Azure Document Intelligence - Mistral OCR - Chandra OCR - DeepSeek OCR - Dots OCR - MinerU - Infinity Parser - PaddleOCR VL - LFM VL Quick Start Python 3.12+ and uv are required. For more in-depth information, see the Getting Started guide. Churro Model and Dataset - Churro 3B VLM exceeds the accuracy of Gemini 2.5 Pro at 15.5x lower cost. - Churro-DS dataset contains 100K pages from 155 historical collections spanning 22 centuries and 46 language clusters. Cost vs. accuracy: Churro (3B) achieves higher accuracy than much larger commercial and open-weight VLMs while being substantially cheaper. The following are pages from the CHURRO dev set, randomly picked from the subset where Churro outperforms Gemini 2.5 Pro ","default_branch":null,"files":null,"tree":[],"storefront":"/r/stanford-oval","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/stanford-oval/Churro/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}