An Offline Signature Recognition and Verification System Based on Neural Network

Abstract

Various techniques are already introduced for personal identification and verification based on different types of biometrics which can be physiological or behavioral. Signatures lies in the category of behavioral biometric which can distort or changed with course of time. Signatures are considered to be most promising authentication method in all legal and financial documents. It is necessary to verify signers and their respective signatures. This paper presents an Offline Signature recognition and verification system(SRVS). In this system signature database of signature images is created, followed by image preprocessing, feature extraction, neural network design and training, and classification of signature as genuine or counterfeit.

Automatic Signature Verifier Using Gaussian Gated Recurrent Unit Neural Network

“…Acquisition Methods. Typically, data are gathered either in online or offline mode for the automatic signature verifiers [20]. Images of signatures are scanned or shot from paper in the offline mode.…”