Signature recognition using fuzzy min-max neural network

The signature recognition system has been inspired from the human capability to recognize any pattern, which is a very difficult task for normal computer system having computing power of more then billions instructions per second. In this system the input is given in the form of a digital image by using writing pad, optical scanner, or digital camera. This input image is processed to extract the information by using data acquisition and HU´s seven moment invariant to the order of three. Then the fuzzy min-max algorithm can applied to classify the signature pattern and this fuzzy min-max algorithm is totally fit to the neural network framework. The neural network middle layer is work as fuzzified neuron and because of this the output can be correctly classified .Use of fuzzy membership function is increase the accuracy of the classification of signature pattern because the decision boundaries are not crisp rather it is fuzzy. The neural network is designed for this work is for the category learning which can increase the speed of recognition because in this fuzzy min-max neural network the supervised learning algorithm is used also it can learn nonlinear class boundaries in a single pass through the data and provides the ability to incorporate new and refine existing classes without retraining. The advantage of our system is its accuracy in recognizing signature is nearly 53% for single signature pattern per class and if the signature pattern per class are increased then the accuracy is increased up to 92%.because patterns per class with slighter changes in it can increase the recognition efficiency naturally and there is no increase in training time because the neural network used is for category learning in which the dataset & index of the class is used for training.