An approach to empirical Optical Character Recognition paradigm using Multi-Layer Perceptorn Neural Network

In this paper we are represent the architecture of Optical Character Recognition that converting from visual character to the machine readable format. To present this architecture, several stages are associate like take the character input image, preprocessing the image, feature extraction of the image and at last take a decision by the artificial computational model same as biological neuron network. Decision making system by the Artificial Neural Network associated with two steps; first is adapted the artificial neural network throughout the Multi-Layer Perceptron learning algorithm and second is recognition or classification process for the character image to comprehensible for the machine in a way that what character is it. Our proposal architecture achieved 91.53% accuracy to recognize the isolated character image and 80.65% accuracy for the sentential case character image.