Character Recognition With Neural Network
Abstract
In this project artificial neural network has been called for its application as characters recognizing network. The network is made to learn as per the requirement by training them with some specific patterns that corresponds to the character. The number of input and output layer neurons is chosen. The training patterns and testing patterns are designed using matrices 0s and 1s. The weights in the network are adjusted using back propagation algorithm (delta rule) for training patterns and are checked for testing patterns. Then we train the network using those input patterns followed by testing the neural network with given training patterns.References
McCulloch, Pitts. A logical calculus of the ideal imminent nervous activity, Bull Math Biophys.
M.L. Minsky, S. Papert. Perceptions Cambridge. MA: MIT Press; 1969.
Pitts, W.W. McCulloch. How we know universals, Bull Math Biophys. 1947.
B. Widrow, Angell. Reliable Networks for Computing and Control Aerospace Engineering.
T. Kohenen. Self-organisation and associative memory: series in information sciences. 1984.
M.A. Cohen, Grossberg. Absolute stability of global pattern formation and parallel memory storage by competitive neural networks.
J. Hopfield. J Neural Networks and physical systems with emergent collective computational abilities.
F.J. Pieda. Generalization of back propagation to recurrent neural networks.
B.K. Verma. New methods of training the MLP.
J.J. Mulawka. Improving the training time of the back propagation algorithms.
D.W. Patterson. Artificial intelligence and expert system.
G.L. Martin, J.A. Pittman. Recognition hand-printed letters and digits using back-propagation learning.
J.P. Nadal. Learning in feedforward layered networks: the algorithms.
G.K. Pieda. Generalization of back propagation.
