  In this paper we propose an artificial intelligence imaging solution to
support archaeologists in the classification task of Roman commonware
potsherds. Usually, each potsherd is represented by its sectional profile as a
two dimensional black-white image and printed in archaeological books related
to specific archaeological excavations. The partiality and handcrafted variance
of the fragments make their matching a challenging problem: we propose to pair
similar profiles via the unsupervised hierarchical clustering of non-linear
features learned in the latent space of a deep convolutional Variational
Autoencoder (VAE) network. Our contribution also include the creation of a
ROman COmmonware POTtery (ROCOPOT) database, with more than 4000 potsherds
profiles extracted from 25 Roman pottery corpora, and a MATLAB GUI software for
the easy inspection of shape similarities. Results are commented both from a
mathematical and archaeological perspective so as to unlock new research
directions in both communities.
