Text-independent writer recognition using modified texture and microstructure features

  • Dongli Wang
  • , Zhimin Huang*
  • , Yue Lu
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In this paper, we propose effective techniques for automatic writer recognition that operate at different analysis levels. At the texture level, we present a novel 2-D wavelet-based method to replace the traditional 2-D Gabor filter. In our analysis at the microstructure level, we use a floating grid around every edge pixel to record orientation and curvature information to characterize writer individuality. The final unique distance between two handwritten samples is computed as the average of the distances between the individual features. The experiments on a database containing 203 writers of address images exhibit that proposed methods achieve promising results.

Original languageEnglish
Title of host publicationPIC 2014 - Proceedings of 2014 IEEE International Conference on Progress in Informatics and Computing
EditorsYinglin Wang, Xuelong Li, Hongming Cai
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages75-78
Number of pages4
ISBN (Electronic)9781479920334
DOIs
StatePublished - 2 Dec 2014
Event2014 2nd IEEE International Conference on Progress in Informatics and Computing, PIC 2014 - Shanghai, China
Duration: 16 May 201418 May 2014

Publication series

NamePIC 2014 - Proceedings of 2014 IEEE International Conference on Progress in Informatics and Computing

Conference

Conference2014 2nd IEEE International Conference on Progress in Informatics and Computing, PIC 2014
Country/TerritoryChina
CityShanghai
Period16/05/1418/05/14

Keywords

  • Combination
  • the micro-structure level
  • the texture level
  • writer recognition

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