In digital paleography, recent technology advancements are used to support paleographers in the study and analysis of ancient documents. One main goal of paleographers is to identify the different scribes (writers) who wrote a given manuscript. Deep learning has recently been applied to many domains. However, in order to overcome its requirement of large amount of labeled data, transfer learning have been used. This approach typically uses previously trained large deep networks as starting points to solve specific classification problems. In this paper, we present a two step deep transfer learning based tool to help paleographers identify the parts of a manuscript that were written by the same writer. The suggested 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.

A Two-Step System Based on Deep Transfer Learning for Writer Identification in Medieval Books

Cilia, Nicole Dalia;
2019

Abstract

In digital paleography, recent technology advancements are used to support paleographers in the study and analysis of ancient documents. One main goal of paleographers is to identify the different scribes (writers) who wrote a given manuscript. Deep learning has recently been applied to many domains. However, in order to overcome its requirement of large amount of labeled data, transfer learning have been used. This approach typically uses previously trained large deep networks as starting points to solve specific classification problems. In this paper, we present a two step deep transfer learning based tool to help paleographers identify the parts of a manuscript that were written by the same writer. The suggested 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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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11387/153637
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