Topological analysis of ancient glyphs

This paper presents a machine learning approach to explore the phenetic relations of historical scripts and their glyphs. Its first step is the identification of the observable topological transformations in the development of the glyphs, and with the use of these transformations, the method collects the possible cognate glyphs by minimizing the necessary topological transformations between the glyphs. In these investigations, the phonetic properties of the graphemes were consistently considered. The second step of our method is selecting similarity groups of possible cognate glyphs by minimizing the differences of their topological properties. The third step is multidimensional scaling and different cluster analyses based on the similarity groups of the glyphs of the historical scripts in order to explore the phenetic relationships between these scripts. The resulting phenetic structure of the scripts could be used for paleographical research, especially in deciphering ancient hard-to-read inscriptions.