Handwriting-based writer identification with complex wavelet transform

• Published in: 2008 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
• Language: English
• 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
• ISBN: 9781424422388; 1424422388
• ISSN: 2158-5695
• DOI: 10.1109/ICWAPR.2008.4635849

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.