A Novel Method for Signature Verification Using Deep Learning
Ali Fathel Rasheed, Ahmed Maamoon Alkababji · Webology · 2022
In general, signatures are used to authenticate an individual's authenticity. A solid way of certifying the legitimacy of a signature is still waiting for. The approach offered in this paper will allow people to identify signatures to determine whether a signature is forged or genuine. We have tried to automate the signature verification procedure using Convolutionary Neural Networks in our system. In computer applications such as picture registration and identification of objects, image classification and recovery, feature detectors and descriptors have a key role to play. This article discusses the performance analysis of numerous characteristic and descriptor detectors like SIFT, SURF, ORB, BRIEF, BRISK, FREAK. The number of matching points in areas overlapping between images and subjective stitching accuracy were analysed in terms of the number of features. The removal of varied symmetrical characteristics of the image enhances the possibility that different scene views can reliably satisfy the requirements. One experiment result show that the maximum number of detected key points is AGAST, FAST and BRISK detection, but the lesser number of extracted key points is STAR, AKAZE and MSER. In addition, each algorithm's speed is recorded.