Character Detection using YOLO for Writer Identification in multiple Medieval books

TL;DR

The study tackles writer identification in medieval manuscripts by replacing earlier template matching and CNN approaches with a YOLOv5s6 object detector to localize occurrences of the letter 'a' produced by a specific scribe. It demonstrates that fine-tuning on scribe-specific instances yields more detections than previous methods and enables confidence-score based attribution across manuscripts, enabling cross-bible inferences without an explicit writer classifier. The experimental results show up to 92.64% accuracy at an optimized confidence threshold, highlighting the utility of detection confidence as a decision signal for scribal attribution. This work advances automated paleography by providing a scalable, data-driven framework for rich annotation and cross-document writer analysis in medieval script.

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.

Figures (2)

• Figure 1: Proposed method.
• Figure 2: Accuracy and F-score values for different confidence thresholds.