A Page-Based Reject Option for Writer Identification in Medieval Books

Abstract

One main goal of paleographers is to identify the different writers who wrote a given manuscript. Recently, paleographers are starting to use digital tools which provide new and more objective ways to analyze ancient documents. On the other hand, in the last few years, deep learning techniques have been applied to many domains and to overcome its requirement of a large amount of labeled data, transfer learning has been used. This latter approach uses previously trained large deep networks as starting points to solve specific classification problems. In this paper, we present a novel approach based on deep transfer learning to implement a reject option for the recognition of the writers in medieval documents. The implemented option is page-based and considers the row labels provided by the trained deep network to estimate the class probabilities. The proposed approach has been tested on a set of digital images from a Bible of the XII century. The achieved results confirmed the effectiveness of the proposed approach.