Deep Aramaic: Towards a Synthetic Data Paradigm Enabling Machine Learning in Epigraphy
This work addresses the data scarcity issue for researchers in epigraphy, enabling more accurate interpretation of ancient inscriptions, though it is incremental as it applies synthetic data generation to a specific domain.
The paper tackles the problem of scarce labeled data for training machine learning models in epigraphy by generating synthetic photo-realistic datasets of Old Aramaic letters, resulting in a ResNet model that achieves high accuracy in classifying real, degraded inscriptions from the 8th century BCE Hadad statue.
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.