Field archeologists are called upon to identify potsherds, for which they rely on their professional experience and on reference works. We have developed a recognition method starting from images captured on site, which relies on the shape of the sherd's fracture outline. The method sets up a new target for deep-learning, integrating information from points along inner and outer surfaces to learn about shapes. Training the classifiers required tackling multiple challenges that arose on account of our working with real-world archeological data: paucity of labeled data; extreme imbalance between instances of different categories; and the need to avoid neglecting rare classes and to take note of minute distinguishing features of some classes. The scarcity of training data was overcome by using synthetically-produced virtual potsherds and by employing multiple data-augmentation techniques. A novel form of training loss allowed us to overcome classification problems caused by under-populated classes and inhomogeneous distribution of discriminative features.

We describe an automated pipeline for digitization of catalog drawings of pottery types. This work is aimed at extracting a structured description of the main geometric features and a 3D representation of each class. The pipeline includes methods for understanding a 2D drawing and using it for constructing a 3D model of the pottery. These will be used to populate a reference database for classification of potsherds. Furthermore, we extend the pipeline with methods for breaking the 3D model to obtain synthetic sherds and methods for capturing images of these sherds in a way that matches the imaging methodology of archaeologists. These will serve to build a massive set of synthetic sherd images that will help train and test future automated classification systems.