HieroGlyphTranslator: Automatic Recognition and Translation of Egyptian Hieroglyphs to English

TL;DR

This paper presents a deep learning-based method for automatic recognition and translation of Egyptian hieroglyphs into English, achieving a BLEU score of 42.2, which surpasses previous results.

Contribution

It introduces a three-stage approach combining segmentation, symbol mapping, and CNN-based translation for hieroglyph recognition and translation.

Findings

Achieved BLEU score of 42.2 in translation accuracy

Utilized two datasets for classification and translation tasks

Demonstrated significant improvement over prior research

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.