A hierarchical neural network architecture for handwritten numeral recognition

This paper presents a hierarchical neural network architecture for recognition of handwritten numeral characters. In this new architecture, two separately trained neural networks connected in series, use the pixels of the numeral image as input and yield ten outputs, the largest of which identifies the class to which the numeral image belongs. The first neural network generates the principal components of the numeral image using Oja's rule, while the second neural network uses an unsupervised learning strategy to group the principal components into distinct character clusters. In this scheme, there is more than one cluster for each numeral class. The decomposition of the global network into two independent neural networks facilitates rapid and efficient training of the individual neural networks. Results obtained with a large independently generated data set indicate the effectiveness of the proposed architecture. Copyright (C) 1997 Pattern Recognition Society.