One of the oldest and most well-known biometric testifying procedures in modern culture is the authentication of handwritten signatures. The field is divided into areas that operate online and offline depending on the acquisition procedure. In online signature verification, the entire signing procedure is carried out using some sort of acquisition equipment, whereas offline signature verification just uses scanned photographs of the signatures. In this paper, we propose an image-based offline signature realization and verification system. Support Vector Machine and artificial neural network are both employed to support the goal intended for this thesis. Modern better processes for features extraction are presented. Two independent sequential neural networks are created, one for verifying and the other for recognizing signatures (i.e. for detecting forgery). A recognition network regulates the parameters of the verification network, which are generated separately for each signature. A signature code and acceptable dataset are used to rigorously validate the System's overall performance.
Offline signature verification, GPDS, Hus Moment, Radon Transform, ANN, SVM
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