TY - JOUR
T1 - Writer identification of Chinese handwriting documents using hidden Markov tree model
AU - He, Zhenyu
AU - You, Xinge
AU - Tang, Yuan Yan
N1 - Funding Information:
This work was supported by research grants received from Research Grant Council (RGC) of Hong Kong, and Faculty Research Grant (FRG) of Hong Kong Baptist University. This work is supported by Grants 60403011 and 60773187 from the NSFC, NCET2007 and Grants 2006ABA023, 2007ABA036, 2007CA011 from the Department of Science and Technology in Hubei province, China. In addition, the authors would like to thank the anonymous reviewers for their perceptive comments, which have significantly improved the paper.
PY - 2008/4
Y1 - 2008/4
N2 - 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.
AB - 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.
KW - Writer identification
KW - Chinese document
KW - Wavelet
KW - Hidden Markov tree model
KW - Two-dimensional Gabor model
UR - http://www.scopus.com/inward/record.url?scp=36749060017&partnerID=8YFLogxK
U2 - 10.1016/j.patcog.2007.08.017
DO - 10.1016/j.patcog.2007.08.017
M3 - Journal article
AN - SCOPUS:36749060017
SN - 0031-3203
VL - 41
SP - 1295
EP - 1307
JO - Pattern Recognition
JF - Pattern Recognition
IS - 4
ER -