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CHURRO: Making History Readable with an Open-Weight Large Vision-Language Model for High-Accuracy, Low-Cost Historical Text Recognition
Handwritten and printed text recognition across 22 centuries and 46 language clusters, including historical and dead languages.
Cost vs. accuracy: CHURRO (3B) achieves higher accuracy than much larger commercial and open-weight VLMs while being substantially cheaper.
CHURRO is a 3B-parameter open-weight vision-language model (VLM) for historical document transcription. It is trained on CHURRO-DS, a curated dataset of ~100K pages from 155 historical collections spanning 22 centuries and 46 language clusters. On the CHURRO-DS test set, CHURRO delivers 15.5× lower cost than Gemini 2.5 Pro while exceeding its accuracy.
For more details and code see https://github.com/stanford-oval/Churro.
License: Due to licensing restrictions on the original datasets used in Churro, use is permitted for research purposes only.
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