CHURRO: Making History Readable with an Open-Weight Large
  Vision-Language Model for High-Accuracy, Low-Cost Historical Text Recognition
Authors: name: Sina J. Semnani, name: Han Zhang, name: Xinyan He, name: Merve Tekgürler, name: Monica S. Lam
Organization: Stanford Open Virtual Assistant Lab (OVAL)

Accurate text recognition for historical documents can greatly advance the
study and preservation of cultural heritage. Existing vision-language 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 3B-parameter open-weight VLM specialized for
historical text recognition. The model is trained on CHURRO-DS, 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
second-best model, Gemini 2.5 Pro, by 1.4% and 6.5%, respectively, while being
15.5 times more cost-effective.
  By releasing the model and dataset, we aim to enable community-driven
research to improve the readability of historical texts and accelerate
scholarship.