Authors:
Lucia Lombardi
;
Francesco Mercaldo
and
Antonella Santone
Affiliation:
Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, Campobasso, Italy
Keyword(s):
Archeology, Hieroglyphs, Deep Learning, Object Detection, YOLO.
Abstract:
Old Egyptians used Hieroglyphic language to record their findings in medicine, engineering, sciences, achievements, their religious views, beside facts from their daily life. Thus, it is fundamentally important to understand and digitally store these scripts for anyone who wants to understand the Egyptian history and learn more about this great civilization. The interpretation of Egyptian hieroglyphs is areasonably broad and highly complex problem, but have always been fascinating with their stories and the ability to be read in several ways rather than one, which is a challenge in itself to be translated to modern languages. In this paper, we adopt the YOLO 8 model which revolutionized object detection with its one-stage deep learning approach. YOLO is designed to classify images and accurately determine the positions of detected objects within them. Using this DL approach, we were able to significantly reduce the time required to investigate the interpretation of hieroglyphs. To en
sure the reproducibility of our results, we opted to utilize a publicly available dataset. All the metrics demonstrate the anticipated patterns: precision, recall, mAP 0.5, and mAP 0.5:0.95 are expected to increase as the number of epochs progresses, indicative of the model effectively learning to detect objects from Egyptian hieroglyphs images.
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