Machine learning techniques for exploring influence, commonalities, and shared origin of scripts: cases of Ethiopic, Armenian, Georgian, and Caucasian Albanian scripts

Abstract

The morphological similarities between the Armenian, Georgian, and Caucasian Albanian scripts and the Ethiopic script have long intrigued both casual observers and scholars. However, prior studies have relied primarily on qualitative or historical analysis, often lacking objective or computational rigor. This study addresses that gap by applying machine learning and deep learning methods to explore potential structural relationships among these scripts. Using over 28,000 images of Ethiopic characters, we trained a deep convolutional neural network and augmented the dataset to enhance generalization. The resulting model, FeedelLigence, analyzes cross-script similarities through transformation-invariant distance measures, cosine distance (CD), and mutual information (MI). Our findings indicate notable structural and symbolic proximity between Ethiopic and the three comparison scripts. Armenian showed the strongest similarity, with the highest MI (0.7428 bits) and the lowest CD (0.0774). Georgian and Caucasian Albanian followed, with MI scores of 0.6843 and 0.6561 bits, and CDs of 0.1558 and 0.2498, respectively. These results provide computational evidence of significant structural overlap, suggesting possible historical connections or shared influences. In a broader cultural context, such affinities align with historical patterns of script evolution and cross-civilizational exchange. By combining artificial intelligence with comparative script analysis, this study offers a novel, quantitative perspective on the relationships among ancient writing systems—advancing our understanding beyond traditional human-centered approaches.