Feedforward neural network for handwritten character recognition
Janusz A. Starzyk, N. Ansari · 2003
An analysis of feedforward neural networks for handwritten character recognition was performed to improve the learning capability and accuracy of classification, which are limiting factors of back-propagation. The authors describe two methods which attempt to tackle the shortcomings of back-propagation yet keep the feedforward organization of the neural network. These methods give results comparable to back-propagation, while requiring less training time and a simpler architecture. The first method rejects any pattern which differs from the training data more than a threshold, established during training. The second method involves clustering techniques selecting the most representative patterns as cluster centers. Both methods present the design of a neural network for handwritten digit recognition, and are based on the Parzen window estimates defining the vector space for different classes.>