MAAT: An interactive environment for the extraction, classification, and translation of hieroglyphic signs
DOI:
https://doi.org/10.17979/ja-cea.2026.47.13694Keywords:
Image processing, Neural networks, Machine learning, Artificial intelligence techniques, Computer visionAbstract
Automatic processing of Ancient Egyptian hieroglyphic inscriptions remains challenging because real epigraphic material combines irregular layouts, variable writing direction, damaged surfaces, carved or painted supports, and heterogeneous image quality. Existing approaches often address segmentation, classification or translation as separate tasks, whereas practical epigraphic workflows require inspection and evaluation of the full chain from image processing to translation. This paper presents an interactive research workbench for hieroglyphic sign extraction, classification and translation-oriented evaluation. The implemented workflow demonstrates how interactive supervision can make the complete recognition and translation pipeline auditable at sign, ROI and inscription levels, enabling controlled comparison of extraction configurations and incremental construction of manually validated hieroglyphic datasets.
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Copyright (c) 2026 Raúl Fuentes-Ferrer, Jaime Duque-Domingo, Pedro Javier Herrera
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