Handwritten Digit Recognition Using Adaptive Classifier Construction Techniques

Optical character recognition (OCR) is a classic example of a decision making problem where class identities of image objects are to be determined. This concerns essentially finding a decision function that returns the correct classification of input objects. This chapter proposes a method of constructing such functions by using an adaptive learning framework based on a multilevel classifier synthesis schema. The schema’s structure and the way classifiers on a higher level are synthesized from those on lower levels are subject to an adaptive iterative process that allows learning from input training data. Detailed algorithms and classifiers based on similarity and dissimilarity measures are presented. Also, results of computer experiments using the techniques described on a large handwritten digit database are included as an illustration of the application of the proposed methods.