Off-line Signature Recognition Using Weightless Neural Network and Feature Extraction


The problem of automatic signature recognition and verification has been extensively investigated due to the vitalityof this field of research. Handwritten signatures are broadly used in daily life as a secure way for personal identification. In thispaper a novel approach is proposed for handwritten signature recognition in an off-line environment based on WeightlessNeural Network (WNN) and feature extraction. This type of neural networks (NN) is characterized by its simplicity in design andimplementation. Whereas no weights, transfer functions and multipliers are required. Implementing the WNN needs onlyRandom Access Memory (RAM) slices. Moreover, the whole process of training can be accomplished with few numbers oftraining samples and by presenting them once to the neural network. Employing the proposed approach in signature recognitionarea yields promising results with rates of 99.67% and 99.55% for recognition of signatures that the network has trained on andrejection of signatures that the network .has not trained on, respectively