Multi-layer Classification Approach for Online Handwritten Gujarati Character Recognition

Abstract

In this paper, the authors present a multi-layer classification approach for online handwritten character recognition for the Gujarati characters. The Gujarati language consists of many confusing characters which lead to misclassification. Multi-layer classification technique is proposed to increase the accuracy of confusing characters. In the first layer of classification, SVM classifier with the polynomial kernel is used with all training data. If first layer classifier returns a character which can be confused with some characters than in the second layer, SVM with the linear kernel is used with confusing characters’ training data. A hybrid feature set consisting zoning features and dominant point-based normalized chain code feature is used in both layers of classification. The system is trained using a data set of 2000 samples and tested by 200 different writers. The authors have achieved an average accuracy of 94.13% with an average processing time of 0.103 s per stroke.