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Objective of this paper is to study the character recognition capability of feed-forward back-propagation algorithm using more than one hidden layer. This analysis was conducted on 182 different letters from English alphabet. After binarization, these characters were clubbed together to form training patterns for the neural network. Network was trained to learn its behavior by adjusting the connection strengths on every iteration. The conjugate gradient descent of each presented training pattern was calculated to identify the minima on the error surface for each training pattern.
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