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IJDAR
2002

Performance evaluation of pattern classifiers for handwritten character recognition

10 years 4 months ago
Performance evaluation of pattern classifiers for handwritten character recognition
Abstract. This paper describes a performance evaluation study in which some efficient classifiers are tested in handwritten digit recognition. The evaluated classifiers include a statistical classifier (modified quadratic discriminant function, MQDF), three neural classifiers, and an LVQ (learning vector quantization) classifier. They are efficient in that high accuracies can be achieved at moderate memory space and computation cost. The performance is measured in terms of classification accuracy, sensitivity to training sample size, ambiguity rejection, and outlier resistance. The outlier resistance of neural classifiers is enhanced by training with synthesized outlier data. The classifiers are tested on a large data set extracted from NIST SD19. As results, the test accuracies of the evaluated classifiers are comparable to or higher than those of the nearest neighbor (1-NN) rule and regularized discriminant analysis (RDA). It is shown that neural classifiers are more susceptible to s...
Cheng-Lin Liu, Hiroshi Sako, Hiromichi Fujisawa
Added 22 Dec 2010
Updated 22 Dec 2010
Type Journal
Year 2002
Where IJDAR
Authors Cheng-Lin Liu, Hiroshi Sako, Hiromichi Fujisawa
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