A COMPARISON OF ARTIFICIAL NEURAL NETWORK AND K-NEAREST NEIGHBOR CLASSIFIERS IN THE OFF-LINE SIGNATURE VERIFICATION
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Abstract
The ANN is a new classification technique within the field of applied mathematics learning theory that has been applied successfully in pattern recognition applications like signature, face and speaker recognition, whereas the k-NN may be a non parametric technique used for classification of handwriting recognition and signature verification. This paper reports on a comparison of the 2 classifiers in off-line signature verification. For this purpose, associate acceptable learning and testing protocol was created to watch the potential of the classifiers to soak up intrapersonal variability and highlight social similarity using random, easy and simulated forgeries by finding the accuracy.
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