Shaping History: Advanced Machine Learning Techniques for the Analysis and Dating of Cuneiform Tablets over Three Millennia - Models

Our research leverages advanced deep learning methods to classify cuneiform tablets by their historical periods, focusing on shape analysis rather than textual content. Utilizing a dataset of over 94,000 images from the Cuneiform Digital Library Initiative, we introduce a novel toolset powered by Variational Auto-Encoders (VAEs) to enhance model interpretability. By highlighting the predictive power of tablet silhouettes for historical period classification with a ResNet model reaching 61\% macro-F1 score,, our approach allows researchers to explore changes in tablet shapes across different eras. This methodology not only complements traditional archaeological methods but also enriches the field of document analysis and diplomatics, offering valuable tools for historians and epigraphists to understand ancient Mesopotamian cultures. The models attached include: Trained ResNet50 model Trained VAE model