Comparison of Feature Extraction Methods for Recognition of Isolated Handwritten Characters in Gurmukhi Script

The present paper is a comparative study of different feature extraction techniques for recognition of isolated handwritten characters in Gurmukhi script. The whole process consists of three stages. The first, feature extraction stage, analyzes the set of isolated characters and select the set of features that can be used to uniquely identify characters. For the selection of stable and representative set of features of character under consideration in this problem Zoning, Directional Distance Distribution (DDD) and Gabor methods have been used. The second stage is classification stage which uses features extracted in the first stage to identify the character. For classification Support Vector Machine (SVM) has been used to identify the character. In the third stage, feature extraction methods have been compared with respect to recognition rate. An annotated sample image database of isolated handwritten characters in Gurmukhi script has been prepared which has been used for training and testing of the system. Gabor based feature extraction proved to be better as compared to others.