Novel preprocessing techniques as an aid to hand-printed character recognition
J. Nellis, T.J. Stonham · 1991
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.>