Handwriting-based personal identification

Zhenyu He, Xinge You*, Yuan Yan Tang, Bin Fang, Jianwei Du

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review


Handwriting-based personal identification, which is also called handwriting-based writer identification, is an active research topic in pattern recognition. Despite continuous effort, offline handwriting-based writer identification still remains as a challenging problem because writing features can only be extracted from the handwriting image. As a result, plenty of dynamic writing information, which is very valuable for writer identification, is unavailable for offline writer identification. In this paper, we present a novel wavelet-based Generalized Gaussian Density (GGD) method for offline writer identification. Compared with the 2-D Gabor model, which is currently widely acknowledged as a good method for offline handwriting identification, GGD method not only achieves a better identification accuracy but also greatly reduces the elapsed time on calculation in our experiments.
Original languageEnglish
Pages (from-to)209-225
Number of pages17
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number2
Publication statusPublished - Mar 2006

User-Defined Keywords

  • Writer identification
  • wavelet
  • GGD model
  • Gabor


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