Efficient Hidden Markov Models for Online Handwritten Gurmukhi Script Recognition

Abstract

Handwriting recognition is the process that converts handwritten characters into machine processable format. The work presented in this thesis deals with online handwritten character recognition. Handwritten characters can either be presented to machine online or offline. A good amount of research in this area has been reported for English, Chinese, Japanese and Korean languages. Research on developing online handwriting recognition systems for some of Indian languages, like, Bangla, Hindi, Malyalam, Tamil and Telugu have been conducted by many researchers in past decade. In this thesis, we have worked for the development of online handwritten character recognition system for Punjabi language. This language is popular among the people living in Punjab, a North Indian state and also among Indians living in other parts of the world such as Australia, Canada, New Zealand and USA. Gurmukhi is the script used to write this language. Some researchers, have also worked on building online handwriting recognition systems for Gurmukhi script. This thesis carries forward the work done by these researchers. An effort has also been made to improve the classification strategies for the recognition of Gurmukhi characters in an online handwritten recognition environment. The work done here is organized in seven chapters. and#8232; First chapter introduces the characteristics of Gurmukhi script and highlights the need of developing a handwritten character recognition system for this script. This chapter elucidates major issues in online handwritten character recognition and complexities of these issues. The processes involved in online handwritten character recognition have also been explained. A detailed study of related literature has also been carried out and presented in this chapter. This study has been presented separately for the involved processes. Chapter two has focused on the process of data collection, selection of writers, and how data is stored.