A Novel Hierarchical Technique for Offline Handwritten Gurmukhi Character Recognition

Abstract

The increasing need of a handwritten character recognition system in the Indian offices such as banks, post offices and so forth, has made it an imperative field of research. In present paper, Authors have presented a novel hierarchical technique for isolated offline handwritten Gurmukhi character recognition. A robust feature set of 105 feature elements is proposed under this work for recognition of offline handwritten Gurmukhi characters using four types of topological features, namely, horizontally peak extent features, vertically peak extent features, diagonal features, and centroid features. For classification Support Vector Machines (SVMs) classifier has been used in this work. SVMs classifier has been considered with four different kernels, namely, linear kernel, polynomial kernel, RBF kernel and sigmoid kernel. For training and testing of a classifier, we have used 3,500 samples of isolated offline handwritten Gurmukhi characters written by one hundred different writers. Maximum recognition accuracy of 91.80 % have been achieved with proposed technique, while using PCA feature set and SVM with a linear kernel classifier.