Ancient Egyptian Hieroglyphs Segmentation and Classification with Convolutional Neural Networks

Nowadays, Deep Learning is advancing in any branch of knowledge, allowing to build tools supporting the work of experts in areas apparently far from the information technology field. In this study we exploit this possibility by focusing on ancient Egyptian hieroglyphic texts and inscriptions. In particular, we explore the ability of different convolutional neural networks (CNNs) to segment glyphs and classify pictures of ancient Egyptian hieroglyphs coming from different datasets of images. Regarding classification, three well-known CNN architectures (ResNet-50, Inception-v3 and Xception) were taken into consideration and trained on the available images, using both the paradigm of transfer learning and training from scratch. Moreover, modifying the architecture of one of the previous networks, we developed a specifically dedicated CNN, named Glyphnet, tailoring its complexity to our classification task. Performances were measured using standard metrics, giving significant results for all the tested networks, with the proposed Glyphnet outperforming the others, in terms of performance as well as ease of training and computational saving. The ancient hieroglyphs segmentation was faced in parallel, using a deep neural network architecture known as Mask-RCNN. This network was trained to segment the glyphs, identifying the bounding box, which will be the input for a network for classification. Even though in this paper we focused on the single hieroglyph segmentation and classification tasks, new and profitable perspectives are opened by the application of Deep Learning techniques in the Egyptological field. In this view, the proposed work can be seen as a starting point for the implementation of much more complex goals, such as: coding, recognition and transliteration of hieroglyphic signs; toposyntax of the hieroglyphic signs combined to form words; linguistics analysis of the hieroglyphic texts; recognition of corrupt, rewritten, and erased signs, towards even the identification of the "hand" of the scribe or the school of the sculptor. This work shows how the ancient Egyptian hieroglyphs identification task can be supported by the Deep Learning paradigm, laying the foundation for developing novel information tools for automatic documents recognition, classification and, most importantly, the language translation task.