DeepHadad: Enhancing the Readability of Ancient Northwest Semitic Inscriptions

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.