High accuracy handwritten character recognition system using contour sequence moments
Yuk Ying Chung, Man To Wong · 2002
Contour sequence moments (CSM) have been used in the classification of four closed planar shapes (Gupta and Srinath, 1987). Also a neural network approach for the classification of four closed planar shapes using a contour sequence is described by Gupta et al in the literature. In this paper, a back-propagation neural network based classifier is used in the recognition of handwritten numerals (from 0 to 9) using contour sequence moments. The network utilized is a multilayer perceptron (MLP) with one hidden layer. Experimental results indicate that the neural network approach gives better recognition accuracy as compared with the conventional statistical classifier: the single nearest-neighbour. The performance of the CSM technique was also compared with geometrical moment (GM) invariants. We found that the recognition accuracy for handwritten characters using CSM and the neural network is over 95% while GM invariants and neural network can only give 82%.