Evaluation of Pattern Classiiers for Fingerprint and Ocr Applications

In this paper we evaluate the classiication accuracy of four statistical and three neural network classiiers for two image based pattern classiication problems. These are ngerprint classiication and optical character recognition (OCR) for isolated handprinted digits. The evaluation results reported here should be useful for designers of practical systems for these two important commercial applications. For the OCR problem, the Karhunen-Lo eve (K-L) transform of the images is used to generate the input feature set. Similarly for the ngerprint problem, the K-L transform of the ridge directions is used to generate the input feature set. The statistical classiiers used were Euclidean minimum distance, quadratic minimum distance, normal, and k-nearest neighbor. The neural network classiiers used were multi-layer perceptron, radial basis function, and probabilistic. The OCR data consisted of 7,480 digit images for training and 23,140 digit images for testing. The ngerprint data consisted of 2,000 training and 2,000 testing images. In addition to evaluation for accuracy, the multi-layer perceptron and radial basis function networks were evaluated for size and generalization capability. For the evaluated datasets the best accuracy obtained for either problem was provided by the probabilistic neural network, where the minimum classiication error was 2.5% for OCR and 7.2% for ngerprints.