Speakers
Description
Handwriting classification is usually evaluated through predictive accuracy, yet the structure of its errors can also provide evidence about the visual organization of historical scripts. In this work, we analyze a ConvNeXt-V2 classifier for handwritten ancient Greek letterforms trained with lacuna-based fragmentation and dynamically learned supervised contrastive loss. Rather than treating misclassifications as noise, we study recurring pairwise confusions as indicators of visual proximity between letterforms. Applied to Hellenistic Greek papyri, this analysis reveals strong confusions between visually similar but phonetically distinct letters, including Alpha–Lambda, Omicron–Theta, Iota–Rho, and Tau–Upsilon. These patterns are consistent with Irigoin’s hypothesis of graphic compensation, according to which graphic systems tend to preserve visual distinctiveness between letters with similar phonetic functions. The tendency remains stable across alternative training objectives, papyrus-level splits, and damaged-character evaluation. More broadly, the study shows how handwriting classification can be used not only for recognition, but also as a quantitative analysis of paleographic structure.