Contrastive-to-Self-Supervised: A Two-Stage Framework for Script Similarity Learning

ArXi:2603.06180v1 Announce Type: cross Learning similarity metrics for glyphs and writing systems faces a fundamental challenge: while individual graphemes within invented alphabets can be reliably labeled, the historical relationships between different scripts remain uncertain and contested. We propose a two-stage framework that addresses this epistemological constraint. First, we train an encoder with contrastive loss on labeled invented alphabets, establishing a teacher model with robust discriminative features.