# Neural Style Transfer for Synthesising a Dataset of Ancient Egyptian Hieroglyphs

Source: [Neural Style Transfer for Synthesising a Dataset of Ancient Egyptian Hieroglyphs](<https://www.sota2.com/research/paper/68fa77cb1e209131cea75605>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:26:29.946Z

Authors: Lewis Matheson Creed

Published: 2025-04-02T22:30:45.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2504.02163>)

[PDF](<https://arxiv.org/pdf/2504.02163.pdf>)