Writer identification using global wavelet-based features

Zhenyu He, Xinge You*, Yuan Yan Tang

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

51 Citations (Scopus)


In the human society, it is very important to find out the true writer of an unknown handwriting document. Therefore handwriting-based writer identification has been a hot research topic in pattern recognition field since several decades before. In our research, we find that the global styles of different people's handwritings are obviously distinctive and the histogram of the wavelet coefficients of preprocessed handwriting image can be well characterized by the generalized Gaussian model (GGD) in wavelet domain. As a consequence, in this paper, we propose a new method by combining wavelet transform and GGD model for writer identification of Chinese handwriting document. Tested by our experiment, this method achieves a satisfied identification result and computational efficiency as well.

Original languageEnglish
Pages (from-to)1832-1841
Number of pages10
Issue number10-12
Publication statusPublished - Jun 2008

Scopus Subject Areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

User-Defined Keywords

  • Writer identification
  • Chinese document
  • Wavelet
  • Generalized Gaussian model
  • 2-D Gabor model


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