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Writer Identification for Handwritten Words

  • Shilpa PandeyEmail author
  • Gaurav Harit
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10481)

Abstract

In this work we present a framework for recognizing writer for a handwritten word. We make use of allographic features at sub-word level. Our work is motivated by previous techniques which make use of a codebook. However, instead of encoding the features using the codewords, we exploit the discriminative properties of features that belong to the same cluster, in a supervised approach. We are able to achieve writer identification rates close to 63% on the handwritten words drawn from a dataset by 10 writers. Our work has application in scenarios where multiple writers write/annotate on the same page.

Keywords

Writer identification Clustering Allographic features 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Indian Institute of Technology JodhpurJodhpurIndia

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