Abstract
A challenging problem of off-line text-independent writer identification is that plenty of dynamic writing information with the handwriting images can not be extracted as writing features,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 Generalized Gaussian Distribution(GGD) method was proposed.Compared with the traditional wavelet-based GGD method,the novel method is more efficient on texture extraction due to its time-invariant features and good directional analysis.Experimental results show that the proposed method achieves a better performance of writer identification.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
