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Main Author: Creed, Lewis Matheson
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2504.02163
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author Creed, Lewis Matheson
author_facet Creed, Lewis Matheson
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2504_02163
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Neural Style Transfer for Synthesising a Dataset of Ancient Egyptian Hieroglyphs
Creed, Lewis Matheson
Machine Learning
Computation and Language
Computer Vision and Pattern Recognition
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.
title Neural Style Transfer for Synthesising a Dataset of Ancient Egyptian Hieroglyphs
topic Machine Learning
Computation and Language
Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2504.02163