An alternative to SOCIFS writer identification framework for handwritten authorship

Satrya Fajri Pratama, Azah Kamilah Muda, Ajith Abraham, Noor Azilah Muda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The uniqueness of shape and style of handwriting can be used to identify the significant features in confirming the author of writing. Acquiring these significant features leads to an important research in Writer Identification (WI) domain. This paper is meant to explore the usage of improved discretization method and explore an alternative to Cheap Computational Cost Class-Specific Swarm Sequential Selection (C4S4) WI framework for Swarm Optimized and Computationally Inexpensive Floating Selection (SOCIFS) feature selection technique in order to find the unique significant features. This paper proposes a novel feature selection framework for handwritten authorship. The promising applicability of the proposed framework has been demonstrated and worth to receive further exploration in identifying the handwritten authorship.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
Pages1007-1012
Number of pages6
DOIs
Publication statusPublished - 2013
Event2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 - Manchester, United Kingdom
Duration: 13 Oct 201316 Oct 2013

Publication series

NameProceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013

Conference

Conference2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
Country/TerritoryUnited Kingdom
CityManchester
Period13/10/1316/10/13

Keywords

  • Computationally inexpensive
  • Feature selection
  • Handwritten authorship
  • Significant features
  • Writer identification

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