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2019 | OriginalPaper | Chapter

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

Authors : Nicole Dalia Cilia, Claudio De Stefano, Francesco Fontanella, Claudio Marrocco, Mario Molinara, Alessandra Scotto di Freca

Published in: New Trends in Image Analysis and Processing – ICIAP 2019

Publisher: Springer International Publishing

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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.

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previous chapter Classification of Arabic Poems: from the to the Century

next chapter Minimizing Training Data for Reliable Writer Identification in Medieval Manuscripts

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Title: A Page-Based Reject Option for Writer Identification in Medieval Books
Authors: Nicole Dalia Cilia
Claudio De Stefano
Francesco Fontanella
Claudio Marrocco
Mario Molinara
Alessandra Scotto di Freca
Publisher: Springer International Publishing
Book: New Trends in Image Analysis and Processing – ICIAP 2019
Print ISBN: 978-3-030-30753-0

Electronic ISBN: 978-3-030-30754-7

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-30754-7_19

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