Deep Aramaic: Towards a Synthetic Data Paradigm Enabling Machine Learning in Epigraphy

TL;DR

This paper introduces a synthetic data generation pipeline for Old Aramaic epigraphy, enabling effective machine learning classification of ancient, degraded inscriptions despite limited real training data.

Contribution

It presents a novel method for creating large, realistic synthetic datasets for Old Aramaic, improving ML-based epigraphy analysis without relying on scarce real data.

Findings

ResNet achieved high accuracy on real ancient inscriptions.

Synthetic data enabled robust classification despite degradation.

Model generalized well across different materials and styles.

Abstract

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.