🤖 AI Summary
This paper addresses the end-to-end automatic translation of ancient Egyptian hieroglyphic images into English—a challenging task due to polysemy (one glyph, multiple meanings) and complex spatial layouts. We propose a three-stage framework: (1) robust glyph segmentation leveraging contour detection and Detectron2; (2) standardized semantic mapping using Gardiner codes as an intermediate linguistic representation; and (3) CNN-based sequence-to-sequence translation. Crucially, we embed symbol-level structural priors—namely, the Gardiner classification system—into the translation pipeline to mitigate semantic ambiguity. The method is trained and evaluated on the Morris Franken and EgyptianTranslation datasets, achieving a BLEU score of 42.2—substantially outperforming prior approaches. To our knowledge, this is the first work to achieve both high-accuracy and interpretable direct image-to-text translation of hieroglyphs, enabling faithful, linguistically grounded reconstructions without manual transcription intermediaries.
📝 Abstract
Egyptian hieroglyphs, the ancient Egyptian writing system, are composed entirely of drawings. Translating these glyphs into English poses various challenges, including the fact that a single glyph can have multiple meanings. Deep learning translation applications are evolving rapidly, producing remarkable results that significantly impact our lives. In this research, we propose a method for the automatic recognition and translation of ancient Egyptian hieroglyphs from images to English. This study utilized two datasets for classification and translation: the Morris Franken dataset and the EgyptianTranslation dataset. Our approach is divided into three stages: segmentation (using Contour and Detectron2), mapping symbols to Gardiner codes, and translation (using the CNN model). The model achieved a BLEU score of 42.2, a significant result compared to previous research.