  Field archeologists are called upon to identify potsherds, for which purpose
they rely on their experience and on reference works. We have developed two
complementary machine-learning tools to propose identifications based on images
captured on site. One method relies on the shape of the fracture outline of a
sherd; the other is based on decorative features. For the
outline-identification tool, a novel deep-learning architecture was employed,
one that integrates shape information from points along the inner and outer
surfaces. The decoration classifier is based on relatively standard
architectures used in image recognition. In both cases, training the
classifiers required tackling challenges that arise when working with
real-world archeological data: paucity of labeled data; extreme imbalance
between instances of the 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 the problems caused by under-populated classes and non-homogeneous
distribution of discriminative features.
