Novel preprocessing techniques as an aid to hand-printed character recognition

Summary form only given. Two preprocessing techniques designed to greatly reduce the burden of classification on an artificial neural network have been developed. The first is a transform which is invariant to rotation, size, and breaks in characters. The second is a low-level feature extractor, in which the features have been statistically selected. The resulting output yields a 45% reduction in memory requirement without any degradation in recognition performance.<
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