Comparative Performance of YOLOv8, YOLOv9, YOLOv10, and YOLOv11 for Layout Analysis of Historical Documents Images

The digitalization of historical documents is of interest for many reasons, including historical preservation, accessibility, and searchability. One of the main challenges with the digitization of old newspapers involves complex layout analysis, where the content types of the document must be determined. In this context, this paper presents an evaluation of the most recent YOLO methods for the analysis of historical document layouts. Initially, a new dataset called BHN was created and made available, standing out as the first dataset of historical Brazilian newspapers for layout detection. The experiments were held using the YOLOv8, YOLOv9, YOLOv10, and YOLOv11 architectures. For training, validation, and testing of the models, the following historical newspaper datasets were combined: BHN, GBN, and Printed BlaLet GT. Recall, precision, and mean average precision (mAP) were used to evaluate the performance of the models. The results indicate that the best performer was YOLOv8, with a Recalltest of 81% and an mAPtest of 89%. This paper provides insights on the advantages of these models in historical document layout detection and also promotes improvement of document image conversion into editable and accessible formats.