Abstract
Two kinds of wavelet features are proposed: (a) Kirsch edge enhancement based 2D wavelets and (b) 2D complex wavelets. The two sets of hybrid features are congregated by combining them with the geometrical features for the recognition of handwritten numerals. Experiments conducted on handwritten numeral recognition and verification show that the two hybrid feature sets can achieve high recognition and verification performance. In addition, the merits of the proposed wavelet feature extraction methods are discussed.
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