Writer identification of Chinese handwriting documents using hidden Markov tree model

Zhenyu He, Xinge You, Yuan Yan Tang*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

110 Citations (Scopus)


Handwriting-based writer identification, a branch of biometrics, is an active research topic in pattern recognition. Since most existing methods and models aim to on-line and/or text-dependent writer identification, it is necessary to propose new methods for off-line, text-independent writer identification. At present, two-dimensional Gabor model is widely acknowledged as an effective and classic method for off-line, text-independent handwriting identification, while it still suffers from some inherent shortcomings, such as the excessive calculational cost. In this paper, we present a novel method based on hidden Markov tree (HMT) model in wavelet domain for off-line, text-independent writer identification of Chinese handwriting documents. Our experiments show this HMT method, compared with two-dimensional Gabor model, not only achieves better identification results but also greatly reduces the elapsed time on computation.

Original languageEnglish
Pages (from-to)1295-1307
Number of pages13
JournalPattern Recognition
Issue number4
Publication statusPublished - Apr 2008

Scopus Subject Areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

User-Defined Keywords

  • Writer identification
  • Chinese document
  • Wavelet
  • Hidden Markov tree model
  • Two-dimensional Gabor model


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