Title :
Novel preprocessing techniques as an aid to hand-printed character recognition
Abstract :
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
Keywords :
character recognition; neural nets; artificial neural network; classification; hand-printed character recognition; low-level feature extractor; memory requirement; preprocessing techniques; recognition performance; Artificial neural networks; Character recognition; Computer networks; Degradation; Feature extraction; Information processing;
Conference_Titel :
Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-0164-1
DOI :
10.1109/IJCNN.1991.155544
