In this report, we review different techniques for handwritten numeral recognition. More importantly we develop and test a hand-written numeral recognition system using multiwavelets. Given a black-and-white numeral, we first trace the contour of the numeral. Secondly we normalize and resample the contour points. Thirdly we perform multiwavelet orthonormal shell expansion on the contour points and we get several resolution levels and the average. We use the multiwavelet coefficients as the features to recognize the hand-written numerals. We use the L1 distance as a measure and the nearest neighbour rule as classifier for the recognition. The experimental result shows that it is a feasible way to use multi-wavelet features in handwritten numeral recognition.
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