Abstract
The work proposes a novel approach for recognition of unconstrained handwritten numerals using the biorthogonal spline wavelets Cohen-Daubechies-Feauveau (CDF) 3/7 as a feature extractor and a multilayer cluster neural network as a classifier. Experiments with the CENPARMI database show that this method yields good results.
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