Unconstrained Kannada handwritten character recognition using multi-level SVM classifier

Abstract

This paper presents an efficient zoning based method for recognition of handwritten Kannada characters using two sets of features, namely, crack codes (the line between the object pixel and background) and the density of the object pixels. A multi-level SVM is used for the classification purpose. The proposed method is implemented in two stages. In the first stage, similar shaped characters are combined into groups resulting in 22 classes instead of 49 classes, one class per character. Crack codes are used to assign the input character image to one of the groups. In the second stage, object pixel density is used to assign label to the input character image within that identified group. Experiments are performed on handwritten Kannada characters consisting of 24500 images with 500 samples for each character. Five-fold cross validation is used for result computation and average recognition rate of 91.02 % is obtained. © Springer-Verlag 2013.