The Paleographer’s Eye ex machina: Using Computer Vision to Assist Humanists in Scribal Hand Identification

The steady digitization of medieval manuscripts is rapidly changing the field of paleography, challenging existing assumptions about handwriting and book production. This development has identified historically important centers for the production of scribal texts, and even individual scribes themselves. For example, scholars of late medieval English literature have identified the copyists of a number of literary manuscripts, and the important role of London government clerks in shaping literary culture. However, traditional paleography has no agreed-upon methodology or fixed criteria for the attribution of handwriting to a particular community, period, or scribe. The approach taken by paleographers is inherently qualitative and subject to personal bias. Even those wielding the mighty "paleographer’s eye" cannot claim objectivity. Computer vision offers solutions with spectacular performance on writer identification and retrieval benchmarks, but these have not been widely adopted by the paleography community because they tend not to hold up in practice. In this work, we attempt to bridge the divide with a software package designed not to automate paleography, but to augment the paleographer’s eye. We introduce automated handwriting identification tools for which the results can be quickly visually understood and assessed, and used as one feature among many by expert paleographers when attributing previously unknown scribal hands. We also demonstrate a use case for our software by analyzing several items believed to be written by Thomas Hoccleve, a highly productive clerk of the Privy Seal who is also an important fifteenth-century English poet.