An approach to empirical Optical Character Recognition paradigm using Multi-Layer Perceptron Neural Network
18th International Conference on Computer and Information Technology (ICCIT), 21-23 December 2015, Military Institute of Science and Technology, Dhaka, Bangladesh, Paper ID# 82, PP. 132-137.
In this paper, we represent the architecture of Optical Character Recognition that converting from visual character to machine-readable format. To present this architecture, several stages are associated 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.