Neural Style Transfer for Synthesising a Dataset of Ancient Egyptian Hieroglyphs

Abstract

The limited availability of training data for low-resource languages makes applying machine learning techniques challenging. Ancient Egyptian is one such language with few resources. However, innovative applications of data augmentation methods, such as Neural Style Transfer, could overcome these barriers. This paper presents a novel method for generating datasets of ancient Egyptian hieroglyphs by applying NST to a digital typeface. Experimental results found that image classification models trained on NST-generated examples and photographs demonstrate equal performance and transferability to real unseen images of hieroglyphs.