Contrastive Attention Networks for Attribution of Early Modern Print
Nikolai Vogler Kartik Goyal Kishore PV Reddy Elizaveta Pertseva Samuel V. Lemley Christopher N. Warren Max G'Sell Taylor Berg-Kirkpatrick
In this paper, we develop machine learning techniques to identify unknown
printers in early modern (c.~1500--1800) English printed books. Specifically,
we focus on matching uniquely damaged character type-imprints in anonymously
printed books to works with known printers in order to provide evidence of
their origins. Until now, this work has been limited to manual investigations
by analytical bibliographers. We present a Contrastive Attention-based Metric
Learning approach to identify similar damage across character image pairs,
which is sensitive to very subtle differences in glyph shapes, yet robust to
various confounding sources of noise associated with digitized historical
books. To overcome the scarce amount of supervised data, we design a random
data synthesis procedure that aims to simulate bends, fractures, and inking
variations induced by the early printing process. Our method successfully
improves downstream damaged type-imprint matching among printed works from this
period, as validated by in-domain human experts. The results of our approach on
two important philosophical works from the Early Modern period demonstrate
potential to extend the extant historical research about the origins and
content of these books.