Contour-based handwritten numeral recognition using multiwavelets and neural networks
In this paper, we develop a handwritten numeral recognition descriptor using multiwavelets and neural networks. We first trace the contour of the numeral, then normalize and resample the contour so that it is translation- and scale-invariant. We then perform multiwavelet orthonormal shell expansion on the contour to get several resolution levels and the average. Finally, we use the shell coefficients as features to input into a feed-forward neural network to recognize the handwritten numerals. The main advantage of the orthonormal shell decomposition is that it decomposes a signal into multiresolution levels, but without down-sampling. Wavelet transforms with down-sampling can give very different coefficients when the input signal is shifted. This is the main limitation of wavelet transforms in pattern recognition. For the shell expansion, we prefer multiwavelets to scalar wavelets because we have two coordinates x and y for each point on the contour. If we extract features from x and y separately, just as Wunsch et al. did (Pattern Recognition 28 (1995) 1237), then we may not get the best features. In addition, we know that multiwavelets have advantages over scalar wavelets, such as short support, orthogonality, symmetry and higher order of vanishing moments. These properties allow multiwavelets to outperform scalar wavelets in some applications, e.g. signal denoising (IEEE Trans. Signal Process. 46 (12) (1998) 3414). We conducted experiments and found that it is feasible to use multiwavelet features in handwritten numeral recognition.
multiwavelets neural networks multiresolution analysis shape representation