Hidden Unit Reduction of Artificial Neural Network on English Capital Letter Recognition

We present an analysis on the minimum number of hidden units that is required to recognize English capital letters of the artificial neural network. The letter font that we use as a case study is the system font. In order to have the minimum number of hidden units, the number of input features has to be minimized. Firstly, we apply our heuristic for pruning unnecessary features from the data set. The small number of the remaining features leads the artificial neural network to have the small number of input units as well. The reason is a particular feature has a one-to-one mapping relationship onto the input unit. Next, the hidden units are pruned away from the network by using the hidden unit pruning heuristic. Both pruning heuristic is based on the notion of the information gain. They can efficiently prune away the unnecessary features and hidden units from the network. The experimental results show the minimum number of hidden units required to train the artificial neural network to recognize English capital letters in system font. In addition, the accuracy rate of the classification produced by the artificial neural network is practically high. As a result, the final artificial neural network that we produce is fantastically compact and reliable