Support vector machine for identification of handwritten Gujarati alphabets using hybrid feature space

Abstract

Gujarati language is used in the western state of Gujarat. Because of its peculiarities, its Optical Character Recognition becomes very difficult. For this language very less work has been done in the area of Optical Character Recognition. In this paper, I have attempted the problem of Optical Character Recognition for handwritten Gujarati alphabets. For this work, forty handwritten alphabets are collected from about one hundred and ninety nine writers. Here; aspect ratio, extent of alphabet, and image subdivision approach has been used as feature space and support vector machine (SVM) has been used for the classification purpose and it gives 86.66 % of performance accuracy. kNN is also used for classification and the result is compared with that of SVM. The paper also describes support vector machine.