DeepHadad: Enhancing the Readability of Ancient Northwest Semitic Inscriptions

Abstract
We present DeepHadad, a novel deep learning approach to improve the readability of severely damaged ancient Northwest Semitic inscriptions. By leveraging concepts of displacement maps and image-to-image translation, DeepHadad effectively recovers text from barely recognizable inscriptions, such as the one on the Hadad statue. A main challenge is the lack of pairs of well-preserved and damaged glyphs as training data since each available glyph instance has a unique shape and is not available in different states of erosion. We overcome this issue by generating synthetic training data through a simulated erosion process, on which we then train a neural network that successfully generalizes to real data. We demonstrate significant improvements in readability and historical authenticity compared to existing methods, opening new avenues for AI-assisted epigraphic analysis.
Paper
In: Eurographics Workshop on Graphics and Cultural Heritage, 2024
Link: https://diglib.eg.org/collections/ab71a152-b620-49fc-a5af-8fed699ef758
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Citation
@Article{Aioanei2024_deep_hadad_short,
author = {Aioanei, Andrei C. and Klein, Jonathan and Klein, Konstantin M. and Hunziker-Rodewald, Regine R. and Michels, Dominik L.},
journal = {Eurographics Workshop on Graphics and Cultural Heritage},
title = {DeepHadad: Enhancing the Readability of Ancient Northwest Semitic Inscriptions},
year = {2024},
month = {9}
}