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A novel hybrid feature extraction method is proposed for the verification of handwritten numerals. The hybrid features consist of one set of two dimensional complex wavelet transform (2D-CWT) coefficients and one set of geometrical features. As 2D-CWT does not only keep wavelet transform's properties of multiresolution decomposition analysis and perfect reconstruction, but also adds its new merits: its magnitudes being insensitive to the small image shifts and multiple directional selectivity, which are useful for handwritten numeral feature extraction. Experiments demonstrated that the features extracted by our proposed method could make the ANN classifier more reliable and convergence easily. A high verification performance has been observed in the series of experiments on handwritten numeral pairs and clusters.