Character Detection using YOLO for Writer Identification in multiple Medieval books

2025-09-08

2025 IEEE International Conference on Cyber Humanities (IEEE-CH)

AI Summary

This study addresses the challenge of identifying scribes in medieval manuscripts by proposing a YOLOv5-based object detection approach to localize and extract discriminative characters—such as the letter “a”—for supporting paleographic dating and stylistic evolution analysis. Departing from conventional template-matching and CNN-based classification pipelines, this work is the first to adapt YOLOv5 to the task of historical handwriting identification. The method substantially increases both the quantity and reliability of detected characters, while a confidence-thresholding mechanism enables robust recognition and rejection of unseen manuscripts. This enhances the system’s generalization capability and improves the accuracy of subsequent writer classification.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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. Very interesting results have been obtained in cases of highly standardized book typologies, where the analysis of basic layout features has allowed high recognition accuracy. However, these layout-based methods are not very general, as their effectiveness often decreases with texts that follow different styles. To address the limitations of layout-based methods, 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 limitation 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, using the EfficientDet architecture, 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.

Problem

• Writer Identification
• Paleography
• Character Detection
• Medieval Manuscripts
• Scribe Attribution

Innovation

• YOLOv5
• writer identification
• character detection
• medieval manuscripts
• paleography