Combinative neural-network-based classifiers for optical handwritten character and letter recognition

This paper compares the similarities and differences between multilayer perceptrons (MLPs) and radial basis function (RBF) neural networks, proposes the method of how to decompose a large-sample and multiple-category training set into many small-sample and two-class training subsets. Further-more, we take single-hidden-layer perceptrons and RBF networks as the basis units to construct combinative neural-network-based classifiers. This kind of combinative classifiers has higher classification accuracy and better generalization performances than their component parts. The results for recognizing the handwritten numerals and English letters show that the presented combinative classifiers are quite effective for solving the large-sample, high-dimensional and multiple-category classification problems.