Self-supervised deep metric learning for ancient papyrus fragments retrieval

This work focuses on document fragments association using deep metric learning methods. More precisely, we are interested in ancient papyri fragments that need to be reconstructed prior to their analysis by papyrologists. This is a challenging task to automatize using machine learning algorithms because labeled data is rare, often incomplete, imbalanced and of inconsistent conservation states. However, there is a real need for such software in the papyrology community as the process of reconstructing the papyri by hand is extremely time-consuming and tedious. In this paper, we explore ways in which papyrologists can obtain useful matching suggestion on new data using Deep Convolutional Siamese-Networks . We emphasize on low-to-no human intervention for annotating images. We show that the from-scratch self-supervised approach we propose is more effective than using knowledge transfer from a large dataset, the former achieving a top-1 accuracy score of 0.73 on a retrieval task involving 800 fragments.