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 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.