Evaluation of pattern classifiers for fingerprint and OCR applications

Abstract

The classification accuracy of four statistical and three neural network classifiers for two image based pattern classification problems is evaluated. These are optical character recognition (OCR) for isolated handprinted digits, and fingerprint classification. It is hoped that the evaluation results reported will be useful for designers of practical systems for these two important commercial applications. For the OCR problem, the Karhunen-Loève (K-L) transform of the images is used to generate the input feature set. Similarly for the fingerprint problem, the K-L transform of the ridge directions is used to generate the input feature set. The statistical classifiers used are Euclidean minimum distance, quadratic minimum distance, normal, and k-nearest neighbor. The neural network classifiers used are multi-layer perceptron, radial basis function, and probabilistic neural network. The OCR data consist of 7480 digit images for training and 23,140 digit images for testing. The fingerprint data used consist of 2000 training and 2000 testing images. In addition to evaluation for accuracy, the multi-layer perceptron and radial basis function networks are evaluated for size and generalization capability. For the evaluated datasets the best accuracy obtained for either problem is provided by a probabilistic neural network. Minimum classification error is 2.5% for OCR and 7.2% for fingerprints.