CHURRO: Making History Readable with an Open-Weight Large Vision-Language Model for High-Accuracy, Low-Cost Historical Text Recognition

Accurate text recognition for historical documents can greatly advance the study and preservation of cultural heritage. Existing visionlanguage models (VLMs), however, are designed for modern, standardized texts and are not equipped to read the diverse languages and scripts, irregular layouts, and frequent degradation found in historical materials. This paper presents CHURRO, a 3Bparameter open-weight VLM specialized for historical text recognition. The model is trained on CHURRO-DS 1 , the largest historical text recognition dataset to date. CHURRO-DS unifies 155 historical corpora comprising 99,491 pages, spanning 22 centuries of textual heritage across 46 language clusters, including historical variants and dead languages. We evaluate several open-weight and closed VLMs and optical character recognition (OCR) systems on CHURRO-DS and find that CHURRO outperforms all other VLMs. On the CHURRO-DS test set, CHURRO achieves 82.3% (printed) and 70.1% (handwritten) normalized Levenshtein similarity, surpassing the secondbest model, Gemini 2.5 Pro, by 1.4% and 6.5%, respectively, while being 15.5 times more costeffective. By releasing the model and dataset, we aim to enable community-driven research to improve the readability of historical texts and accelerate scholarship. 2 1 CHURRO stands for Collection of Historical Unified Resources for Robust OCR. 2 Dataset, model, and code are available at https://gith ub.com/stanford-oval/Churro .