MEH: A Multi-Style Dataset and Toolkit for Advancing Egyptian Hieroglyph Recognition

Maksim Golyadkin, Valeria Rubanova, Aleksandr Utkov, Dmitry Nikolotov, Ilya Makarov; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025, pp. 24488-24496

The recognition of Ancient Egyptian hieroglyphs poses persistent challenges due to limited annotated data and wide stylistic variation. We introduce the Multisource Egyptian Hieroglyphs (MEH) dataset, a new benchmark that captures a diverse range of writing styles with detailed clause-level and OCR annotations derived from expert-curated sources. To address the scarcity of training data, we propose a method for generating synthetic hieroglyphic sequences by combining real linguistic material, hierarchical layout modeling, and diffusion-based rendering. We evaluate both multi-stage and end-to-end OCR systems on MEH, studying the effects of synthetic pretraining, model scale, and cross-style transfer. To enable future expansion, we release pyThoth, an annotation tool with built-in model assistance for streamlined human-in-the-loop labeling. We believe that these contributions lay the groundwork for building an AI-powered research assistant for Egyptology.