Deep Aramaic: Towards a Synthetic Data Paradigm Enabling Machine
  Learning in Epigraphy
Andrei C. Aioanei Regine Hunziker-Rodewald Konstantin Klein Dominik L. Michels
Epigraphy increasingly turns to modern artificial intelligence (AI)
technologies such as machine learning (ML) for extracting insights from ancient
inscriptions. However, scarce labeled data for training ML algorithms severely
limits current techniques, especially for ancient scripts like Old Aramaic. 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. This
comprehensive corpus provides a robust volume of data for training a residual
neural network (ResNet) to classify highly degraded Aramaic letters. The ResNet
model demonstrates high accuracy in classifying real images from the 8th
century BCE Hadad statue inscription. Additional experiments validate
performance on varying materials and styles, proving effective generalization.
Our results validate the model's capabilities in handling diverse real-world
scenarios, proving the viability of our synthetic data approach and avoiding
the dependence on scarce training data that has constrained epigraphic
analysis. Our innovative framework elevates interpretation accuracy on damaged
inscriptions, thus enhancing knowledge extraction from these historical
resources.