Abstract
Paleography is the study of ancient and historical handwriting, its key objectives include the dating of manuscripts and understanding the evolution of writing. Estimating when a document was written and tracing the development of scripts and writing styles can be aided by identifying the individual scribes who contributed to a medieval manuscript. Although digital technologies have made significant progress in this field, the general problem remains unsolved and continues to pose open challenges. ... We previously proposed an approach focused on identifying specific letters or abbreviations that characterize each writer. In that study, we considered the letter "a", as it was widely present on all pages of text and highly distinctive, according to the suggestions of expert paleographers. We used template matching techniques to detect the occurrences of the character "a" on each page and the convolutional neural network (CNN) to attribute each instance to the correct scribe. Moving from the interesting results achieved from this previous system and being aware of the limitations of the template matching technique, which requires an appropriate threshold to work, we decided to experiment in the same framework with the use of the YOLO object detection model to identify the scribe who contributed to the writing of different medieval books. We considered the fifth version of YOLO to implement the YOLO object detection model, which completely substituted the template matching and CNN used in the previous work. The experimental results demonstrate that YOLO effectively extracts a greater number of letters considered, leading to a more accurate second-stage classification. Furthermore, the YOLO confidence score provides a foundation for developing a system that applies a rejection threshold, enabling reliable writer identification even in unseen manuscripts.
Abstract (translated)
古文字学是研究古代和历史上的手写笔迹的学科，其主要目标包括对抄本进行年代鉴定以及理解书写的发展演变。通过识别贡献于中世纪手稿的个人抄写员，可以辅助估算文档的写作时间和追踪脚本及书写风格的变化发展。尽管数字技术在这一领域取得了显著进展，但总体问题仍未解决，并且仍然面临诸多挑战。 我们之前提出了一种专注于识别每个作者特征特定字母或缩写的的方法。在这项研究中，我们考虑了字母“a”，因为它广泛存在于所有文本页面上并且根据古文字学专家的建议具有高度辨识度。我们使用模板匹配技术来检测每一页上的字符"a"出现情况，并利用卷积神经网络（CNN）将每个实例归因于正确的抄写员。 基于之前系统取得的有趣成果以及意识到模板匹配技术需要适当阈值才能工作的限制，我们决定在同一框架下实验采用YOLO目标检测模型来识别不同中世纪书籍中的作者。我们考虑使用YOLO的第五版本来实施YOLO对象检测模型，这完全取代了先前工作中使用的模板匹配和CNN。 实验结果表明，YOLO能够有效地提取更多被考量的字母数量，从而实现更精确的第二阶段分类。此外，YOLO置信度分数为开发应用拒绝阈值系统的奠定了基础，使得在未见过的手稿中进行可靠的作者识别也成为可能。
URL
https://arxiv.org/abs/2601.04834
