Handwritten character recognition based on moment features derived from image partition
Faculty of Sciences. Physics
Los Alamitos :IEEE Computer Soc., 1998
Engineering sciences. Technology
IEEE International Conference on Image Processing, Oct. 04-07, 1998, Chicago, Ill.
University of Antwerp
In this work we present a novel approach to handwritten character recognition which is based on the intuitive way in which characters are written as one or a few continuous lines. Therefore we calculate the zeroth, first and second radial moment as a function of the angle. In practice this is done by dividing the character in 32 angular sections. The three obtained curves can be used for pattern recognition using statistical analysis. The method has been evaluated using the NIST handwritten character data set. At first, a simple chi-square test gave a result of 80.81% recognition rate at zero rejection rate for digits. Using a back-propagation algorithm the recognition rate obtained was 87.54% also at zero rejection rate. Showing that the features are sufficient to discriminate the characters.