"Our research pioneers an innovative methodology for generating synthetic training data tailored to Old Aramaic letters. Our pipeline synthesizes photo-realistic Aramaic letter datasets, incorporating textural features, lighting, damage, and augmentations to mimic real-world inscription diversity. Despite minimal real examples, we engineer a dataset of 250 000 training and 25 000 validation images covering the 22 letter classes in the Aramaic alphabet."

Aioanei AC, Hunziker-Rodewald RR, Klein KM, Michels DL (2024) Deep Aramaic: Towards a synthetic data paradigm enabling machine learning in epigraphy. PLOS ONE 19(4): e0299297. @linguistics

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