Handwriting-based writer identification with complex wavelet transform

Handwriting-based writer identification is a hot research filed in pattern recognition. Off-line text-independent writer identification still remains as a challenging problem because writing features can only be extracted from the handwriting images. As a result, plenty of dynamic writing informatio...

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Bibliographic Details
Published in2008 International Conference on Wavelet Analysis and Pattern Recognition Vol. 2; pp. 597 - 601
Main Authors Da-Yuan Xu, Zhao-Wei Shang, Yuan-Yan Tang, Bin Fang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2008
Subjects
Accuracy
Complex wavelet transform
Distance measurement
Feature extraction
GGD
KL distance
Pattern recognition
Transforms
Wavelet analysis
Wavelet transform
Wavelet transforms
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ISBN9781424422388
1424422388
ISSN2158-5695
DOI10.1109/ICWAPR.2008.4635849

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Summary:Handwriting-based writer identification is a hot research filed in pattern recognition. Off-line text-independent writer identification still remains as a challenging problem because writing features can only be extracted from the handwriting images. As a result, plenty of dynamic writing information, which is very valuable for writer identification, is unavailable for off-line writer identification. This results in high error rate in off-line writer identification. In order to enhance the performance of off-line writer identification, a complex wavelet-based GGD method was presented in this paper. The novel method is based on our discovery that complex wavelet coefficients within each high-frequency sub-band of the handwritings satisfy GGD distribution. Our experiments show the new method, compared with the traditional wavelet-based GGD method, and our method achieves a better performance.
ISBN:9781424422388
1424422388
ISSN:2158-5695
DOI:10.1109/ICWAPR.2008.4635849