Character Recognition by Adaptive Statistical Similarity
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
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This paper describes an approach to classification based on a probabilistic clustering method. Most current classifiers perform classification by modeling class conditional densities directly or by modeling class-dependent discriminant functions. The approach described in this paper uses class-independent multilayer perceptrons (MLP) to estimate the probability that two given feature vectors are in the same class. These probability estimates are used to partition the input into separate classes in a probabilistic clustering. Classification by probabilistic clustering potentially offers greater robustness to different compositions of training and test sets than existing classification methods. Experimental results demonstrating the effectiveness of the method are given for an optical character recognition (OCR) problem. The relationship of the current approach to mixture density estimation, mixture discriminant analysis, and other OCR and handwriting recognition techniques is discussed.