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 shape recognition is proposed for recognition of unconstrained handwritten digits based on various contour information. First, input digits are classified into three groups according to their topological properties. In group 1, the features of normalized length of the digit contour, the normalized area of the digit and Fourier descriptors of the outer contour of a digit are used for recognition of digits. In group 2, the relative position of outer and interior contour centroids, the position of interior contour centroid, the mean and the variance of the outer contour distance function, and Fourier descriptors of the outer contour of a digit are used for the recognition of digits. In group 3, usually there is only one digit 8, but because of the complexity of writing styles, some other digits are still classified into this group. The final recognition is based on shape comparison of the input digit with models. In the recognition process, some special models are established and used for recognition of broken digits and digits whose topological properties are destroyed. In our experiment, 1000 digits from the NIST database are used for training and 5278 unseen digits are used for testing. The recognition rate has reached 98.5% with a reliability 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 Science Ltd.