Handwritten character recognition using gradient feature and quadratic classifier with multiple discrimination schemes

Abstract

In the research of statistical approach for handwritten character recognition, directional element feature (DEF) and modified quadratic discriminant function (MQDF) have been extremely successful and widely used in practical applications. In this paper, we apply several state-of-the-art techniques of handwritten character recognition on this baseline system to improve the recognition accuracy. In feature extraction stage, gradient feature is extracted to replace DEF, which provides higher resolution on both magnitude and angle of the directional strokes in character image. In classification stage, the performance of MQDF classifier is enhanced by multiple discrimination schemes, including minimum classification error (MCE) training on the classifier parameters and modified distance representation for similar characters discrimination. All these techniques we use lead to improvement on the character recognition rate. The performance of the improved recognition system has been evaluated by both handwritten digit recognition and handwritten Chinese character recognition experiments, in which very promising results are achieved.