Evaluation of Egyptian Hieroglyph Classification Across Diverse Writing Styles

摘要

The classification of Egyptian hieroglyphs remains a challenging problem due to the vast variability in writing styles across time periods, regions, and individual scribes. In this work, we present a comprehensive evaluation of hieroglyph classification performance across diverse stylistic domains, highlighting the limitations of current models in generalizing beyond a single style. We introduce a dataset that spans multiple writing styles, ranging from monumental inscriptions to handwritten manuscripts, and assess several near state-of-the-art recognition models. Our analysis reveals significant discrepancies in model performance when exposed to unseen styles, underscoring the need for style-aware learning strategies. This study provides a framework for future research on hieroglyph recognition with a focus on stylistic diversity and serves as a first step toward building vision-language systems capable of analyzing Egyptian hieroglyphic writings.