TECHNOLOGY WATCH:
Ancient alphabets, new insights: Researchers uncover hidden links among the letters. SDSU researchers used AI to compare writing systems across distant regions (San Diego State University).
With artificial intelligence (AI) as an essential tool, San Diego State University researchers have discovered surprising similarities among ancient writing systems from Africa and the Caucasus region of Eurasia. Their study suggests the Armenian alphabet may be more closely related in structure to the ancient Ethiopic writing system than linguists and historians previously thought.
For many years, historians noticed some Armenian, Georgian and Caucasian Albanian letters look similar to letters from Ethiopic, also known as Ge’ez, a writing system developed in the Horn of Africa more than 1,600 years ago. ...
One of the most surprising findings was that the Armenian alphabet appeared almost as similar to Ethiopic as Ethiopic is to its own earlier version. That suggests the resemblance may not be accidental.
That is an interesting discovery. It raises the question whether any cultural influences accompanied the script influence. Not my area, though.
This press release gives a brief, accessible summary of the underlying open-access peer-review article just published in Digital Scholarship in the Humanities. If you're feeling ambitious, you can read the whole, rather technical, article:
Machine learning techniques for exploring influence, commonalities, and shared origin of scripts: cases of Ethiopic, Armenian, Georgian, and Caucasian Albanian scripts
Daniel Zemene, Esatu Zemene, Atharv Sankpal, Eskinder Sahle, Vyshak Athreya Bellur Keshavamurthy, Samuel Kinde Kassegne
Digital Scholarship in the Humanities, fqag029, https://doi.org/10.1093/llc/fqag029
Published: 25 March 2026
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.
Cross-file under Paleography, Ethiopic Watch and Armenian Watch.
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