Neural Style Transfer for Synthesising a Dataset of Ancient Egyptian Hieroglyphs
This addresses data scarcity for low-resource languages like ancient Egyptian, though it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of limited training data for ancient Egyptian hieroglyphs by using Neural Style Transfer to generate synthetic datasets, achieving equal performance in image classification models compared to real photographs.
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.