Egyptian Shabtis Identification by Means of Deep Neural Networks and Semantic Integration with Europeana

Abstract

Ancient Egyptians had a complex religion, which was active for longer than the time that has passed since Cleopatra until our days. One amazing belief was to be buried with funerary statuettes to help the deceased carry out his/her tasks in the underworld. These funerary statuettes, mainly known as shabtis, were produced in different materials and were usually inscribed in hieroglyphs with formulas including the name of the deceased. Shabtis are important archaeological objects which can help to identify the owners, their jobs, ranks or their families. They are also used for tomb dating because, depending on different elements: color, formula, tools, wig, hand positions, etc., it is possible to associate them to a concrete type or period of time. Shabtis are spread all over the world, in excavations, museums or private collections, and many of them have not been studied and identified because this process requires a deep study and reading of the hieroglyphs. Our system is able to solve this problem using two different YOLO v3 networks for detecting the figure itself and the hieroglyphic names, which provide identification and cataloguing. Until now, there has been no other work on the detection and identification of shabtis. In addition, a semantic approach has been followed, creating an ontology to connect our system with the semantic metadata aggregator, Europeana, linking our results with known shabtis in different museums. A complete dataset has been created, a comparison with previous technologies for similar problems has been provided, such as SIFT in the ancient coin classification, and the results of identification and cataloguing are shown. These results are over similar problems and have led us to create a web application that shows our system and is available on line.

Keywords

• shabtis
• computer vision
• YOLO
• Europeana
• CNN
• object classification