Offline Signature Verification Using Support Vector Machine

This project aims at developing a support vector machine for identity verification of offline signature based on the feature values in the database. A set of signature samples are collected from individuals and these signature samples are scanned in a gray scale scanner. These scanned signature images are then subjected to a number of image enhancement operations like binarization, complementation, filtering, and thinning and edge detection. From these pre-processed signatures, features such as centroid, center of gravity, calculation of number of loops, horizontal and vertical profile and normalized area are extracted and stored in a database separately. The values from the database are fed to the support vector machine which draws a hyper plane and classifies the signature into original or forged based on a particular feature value. The developed SVM is successfully tested against 336 signature samples and the classification error rate is less than 7.16% and this is found to be convincing.