Handwritten character recognition by contour sequence moments and neural network
Yuk Ying Chung, Man To Wong, Mohammed Bennamoun · 2002
Contour sequence moments (CSM) have been used in the classification of four closed planar shapes. Gupta et al. described a neural network approach for the classification of four closed planar shapes using a contour sequence. In this paper, a backpropagation neural network is used in the recognition of handwritten numerals (from 0 to 9) using contour sequence moments. Experimental results indicate that the neural network approach gives better recognition accuracy when compared with the two conventional statistical classifiers, namely the nearest neighbour and minimum-mean-distance. This CSM technique was compared with geometrical moment (GM) invariants. We found that the recognition accuracy for handwritten character using GSM and neural network is over 95% while GM invariants and neural network can only give 82%.