HieroGlyphTranslator: Automatic Recognition and Translation of Egyptian Hieroglyphs to English

Ahmed Nasser, Marwan Mohamed, Alaa Sherif, Basmala Mahmoud, Shereen Yehia +3 more
12/3/2025
cs.CVcs.LG

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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.

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Cite this paper

@article{nasser2025hieroglyphtranslator,
  title  = {HieroGlyphTranslator: Automatic Recognition and Translation of Egyptian Hieroglyphs to English},
  author = {Ahmed Nasser and Marwan Mohamed and Alaa Sherif and Basmala Mahmoud and Shereen Yehia and Asmaa Saad and Mariam S. El-Rahmany and Ensaf H. Mohamed},
  year   = {2025},
  eprint = {2512.03817},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2512.03817v1}
}

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