A Quantitative Visual Taxonomy of Worldwide Writing Systems

Quantitatively comparing writing systems at a worldwide scale is challenging because visual similarity must be inferred without relying on predefined linguistic or genealogical assumptions, and only limited ground-truth taxonomy is available for validation. We introduce a fully data-driven framework for comparing scripts based on learned glyph representations. Using a teacher--student model, we obtain deformation-invariant glyph embeddings and aggregate them into script-level distances. To enable principled model selection in the absence of labels, we construct synthetic script datasets that simulate key aspects of script evolution, including partial inheritance, visual distortion, and inventory variation. These benchmarks are used to independently select both the script-level distance metric and the clustering method, showing that nearest-neighbour aggregation and Ward hierarchical clustering best recover controlled relatedness structure. Applying the validated pipeline to 125 historically attested writing systems yields a global visual taxonomy that recovers coherent typological families while also revealing visually driven associations beyond strict genealogy. The resulting structure is stable under controlled perturbations and shows a moderate correlation with independent geographic and temporal metadata, indicating that learned representations capture non-trivial traces of historical transmission. Beyond large-scale comparison, this approach provides a quantitative tool for exploring relationships between writing systems and has potential applications in historical analysis and the decipherment of ancient scripts by identifying visually related alphabets.