A Comprehensive Study on the Recognition of Gurmukhi Script

Abstract

Nowadays, we process all the important information of our lives electronically. Due to the involvement of computers in every sphere there may be a need to develop some efficient and fast techniques so that records can be easily transferred between people and computer systems. Offline text recognition provides an interface between humans and computers. Many researchers are working to recognize the text of Indian scripts like Bangla, Devanagari, Gurmukhi etc. but it is still a challenge to exchange data between people and computers due to the different writing style of the people and very little work has been done for Gurmukhi. In this article different accuracy results are reviewed which are achieved by different researchers using different classification techniques. Various classifiers for the recognition of characters like Support Vector Machine (SVM) based classifier (Upper zone classifier and Lower zone classifier), Hidden Markov Model (HMM) by using a set of features of the normalizedx–ytraces of the stroke, DCT2 feature set using Linear SVM classifier, Polynomial SVM with iDCT2 features, Multi layered perceptron (MLP) neural network andKnearest neighbor (KNN) etc. classifiers have been used.