RECOGNITION OF HANDWRITTEN DIGITS BASED ON CONTOUR INFORMATION

In this paper, a new character recognition method based on topological properties, positions of starting point, statistical analysis, and sh ape recognition is proposed for recognition of unconstrained handwritt en digits based on various contour information. First, input digits ar e classified into three groups according to their topological properti es. In group 1, the features of normalized length of the digit contour , the normalized area of the digit and Fourier descriptors of the oute r contour of a digit are used for recognition of digits. In group 2, t he relative position of outer and interior contour centroids, the posi tion of interior contour centroid, the mean and the variance of the ou ter contour distance function, and Fourier descriptors of the outer co ntour of a digit are used for the recognition of digits. In group 3, u sually there is only one digit 8, but because of the complexity of wri ting styles, some other digits are still classified into this group. T he final recognition is based on shape comparison of the input digit w ith models. In the recognition process, some special models are establ ished and used for recognition of broken digits and digits whose topol ogical properties are destroyed. In our experiment, 1000 digits from t he NIST database are used for training and 5278 unseen digits are used for testing. The recognition rate has reached 98.5% with a reliabilit y rate of 99.09%, a substitution rate of 0.91% and a rejection rate of 0.59%. (C) 1997 Pattern Recognition Society. Published by Elsevier Sc ience Ltd.