From Artifact to Text: A Novel Deep Learning Approach for Ancient Egyptian Language Processing

Ancient Egyptian hieroglyphs represent a writing system of profound historical importance, utilized for over three millennia to record religion, history, administration, and art on papyrus and stone. The task of accurately translating hieroglyphs from images remains a significant challenge, particularly with diverse layouts and orientations encountered on ancient artifacts. While prior research employed computer vision techniques for hieroglyphic recognition and translation, many approaches focused on fixed orientations and writing styles. This paper addresses these limitations by introducing a novel methodology for extracting and arranging hieroglyphic signs from real-world artifacts, irrespective of format or orientation. The proposed solution offers adaptability through two key innovations. First, Layout Analysis Stage processes complex text structures in various row and column combinations. Additionally, this study created the first Hieroglyphic Layout Analysis Dataset covering all main styles and artifact types. Second, a dynamic Sorting Function adjusts to multiple text orientations (left-to-right, right-to-left, top-to-bottom) without predetermined directionality. The "Layout Analysis, Segmentation, and Classification (LASC)" approach accurately extracts and orders hieroglyphic signs from diverse artifacts. LASC was evaluated against authentic ancient Egyptian artifacts, yielding promising results in transcribing hieroglyphic texts with complex layouts and orientations. To the best of authors' knowledge, no existing methodology exhibits this adaptability in handling hieroglyphic layout and orientation complexities.