  This work is focused on improving the character recognition capability of
feed-forward back-propagation neural network by using one, two and three hidden
layers and the modified additional momentum term. 182 English letters were
collected for this work and the equivalent binary matrix form of these
characters was applied to the neural network as training patterns. While the
network was getting trained, the connection weights were modified at each epoch
of learning. For each training sample, the error surface was examined for
minima by computing the gradient descent. We started the experiment by using
one hidden layer and the number of hidden layers was increased up to three and
it has been observed that accuracy of the network was increased with low mean
square error but at the cost of training time. The recognition accuracy was
improved further when modified additional momentum term was used.
