Design and implementation of deep learning strategy based smart signature verification System

Abstract

The signature verification is broadly used for personal identification. The person is identified automatically using signature verification method to avoid forgery persons. The signature verification is classified into the static method and the dynamic method. The static verification method is based on stored images and the dynamic verification method is based on dynamic features of the signature. The integer wavelet transformation method is used to identify the breath and height ratio of the signature features. In addition to that spurious noise also removed before extracting the signature feature. And the signature is isolated from the background of the images. The extracted feature is analyzed using integer wavelet transformation and a neural network is selected to decide according to that original and forgery signature. As compared with the conventional system the proposed found to be about 20% error ratio. The database SVC2004 is selected to verify the signature.