Robust unconstrained handwritten digit recognition using radon transform

The performance of a character recognition system depends heavily on what features are being used. Though many kinds of features have been developed and their test performances on standard database have been reported, there is still room to improve the recognition rate by developing improved features. In this paper, we propose a novel system based on radon transform for handwritten digit recognition. We have used radon function which represents an image as a collection of projections along various directions. The resultant feature vector by applying this method is the input for the classification stage. A Nearest neighbor classifier is used for the subsequent recognition purpose. A test performed on the MNIST handwritten numeral database and on Kannada handwritten numerals demonstrate the effectiveness and feasibility of the proposed method.