Handwritten Gujarati Character Recognition Using Structural Decomposition Technique

Abstract

Handwritten character recognition is the active area of research. Development of Optical Character Recognition (OCR) system for Indian script like Gujarati is still in infancy and hence, there exists many unaddressed challenging problems for research community in this domain. The paper proposes three novel features to represent handwritten Gujarati characters. These features include features extracted based on structural decomposition, zone pattern matching and normalized cross correlation. Methods based on Support Vector Machine (SVM) and Naive Bayes (NB) classifiers have been exercised for the classification of Gujarati characters represented using proposed features. Experiments have been carried out on a dataset of 20500 handwritten Gujarati characters. Experimental results showed significant improvement over state-of-the-art when classifiers were learnt using structural decomposition based features.