Abstract
Accurate text recognition for historical documents can greatly advance the study and preservation of cultural heritage.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.
Abstract (translated)
准确的文字识别对于历史文献的研究和文化遗产的保护具有重要意义。然而，现有的视觉-语言模型（VLMs）主要针对现代标准文本设计，无法处理历史材料中多样化的语言和文字、不规则布局以及频繁出现的质量退化问题。本文介绍了CHURRO，这是一种专为历史文本识别而设计的30亿参数开放权重视觉-语言模型（VLM）。该模型是在迄今为止最大的历史文本识别数据集CHURRO-DS上进行训练的。CHURRO-DS统一了155个历史语料库，包含99,491页文献，涵盖了跨越22世纪、涉及46种语言集群的历史文字遗产，包括历史上变异的语言和已灭绝的语言。 我们评估了几种开放权重和封闭VLM模型以及光学字符识别（OCR）系统在CHURRO-DS上的表现，并发现CHURRO的表现优于所有其他VLM。在CHURRO-DS测试集上，CHURRO在印刷文本中的标准化莱文斯坦相似度得分为82.3%，手写文本得分70.1%，分别超过第二名模型Gemini 2.5 Pro的1.4%和6.5%，并且成本效益高出15.5倍。 通过发布该模型和数据集，我们旨在推动社区驱动的研究以提高历史文献的文字可读性，并加速学术研究的发展。
URL
https://arxiv.org/abs/2509.19768
