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.
Abstract (translated)
低资源语言训练数据的有限可用性使得应用机器学习技术变得具有挑战性。古埃及语就是这样一个资源匮乏的语言。然而，通过创新地使用数据增强方法（如神经风格迁移）可以克服这些障碍。本文提出了一种新颖的方法，利用神经风格迁移对数字字体进行处理来生成古埃及象形文字的数据集。实验结果表明，在使用NST生成的示例和照片训练的图像分类模型在识别真实未见过的象形文字图像时表现出同等的效果和可转移性。
URL
https://arxiv.org/abs/2504.02163
