Abstract
Handwritten text recognition and optical character recognition solutions show excellent results with processing data of modern era, but efficiency drops with Latin documents of medieval times. This paper presents a deep learning method to extract text information from handwritten Latin-language documents of the 9th to 11th centuries. The approach takes into account the properties inherent in medieval documents. The paper provides a brief introduction to the field of historical document transcription, a first-sight analysis of the raw data, and the related works and studies. The paper presents the steps of dataset development for further training of the models. The explanatory data analysis of the processed data is provided as well. The paper explains the pipeline of deep learning models to extract text information from the document images, from detecting objects to word recognition using classification models and embedding word images. The paper reports the following results: recall, precision, F1 score, intersection over union, confusion matrix, and mean string distance. The plots of the metrics are also included. The implementation is published on the GitHub repository.
Abstract (translated)
手写文本识别和光学字符识别解决方案在处理现代数据时表现出色，但在处理中世纪拉丁文文件时效率下降。本文提出了一种深度学习方法，用于从公元9世纪到11世纪的手写拉丁语文档中提取文本信息。该方法考虑到了中世纪文档特有的属性。论文简要介绍了历史文献转录领域的概况、原始数据的初步分析以及相关的工作和研究。文章还阐述了为后续模型训练而开发的数据集步骤，并提供了对处理后的数据进行解释性数据分析的内容。 论文详细说明了从检测对象到使用分类模型和嵌入词图像进行单词识别的深度学习模型提取文档图像文本信息的流程。该文报告了以下结果：召回率、精确度、F1分数、交并比（Intersection over Union）、混淆矩阵以及平均字符串距离，并包括了这些指标的图表。 实现代码已发布在GitHub仓库中。
URL
https://arxiv.org/abs/2512.18865
