PERFORMANCE COMPARISON OF STATISTICAL AND NEURAL NETWORK CLASSIFIERS IN HANDWRITTEN DIGITS RECOGNITION

The major concern of this paper is to compare performance of several statistical and neural network classifiers in both theoretical and practical aspects. Discussion in the latter is based on the results of experiments run with the handwritten digit images from the NIST Special Database 3. The statistical classifiers discussed in the paper can be divided into two types: parametric classifier and non-parametric classifier. The former includes an LDF, a QDF and a RDF, and the latter includes a k-NN. We also adopt an MLP, one of neural network approaches, to compare with the statistical classifiers.