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

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 Datasets attached include: All CDLI IDs used and their URLs on CDLI All tablet silhouettes - Black and white representations A height-to-width ratio table, of the largest component extracted from the silhouettes. VAE encodings per tablet, extracted from the VAE model