Online Handwritten Gurmukhi Character Recognition using Support Vector Machine

Abstract

Communication is always an important part of our life, either in the form of speech or writing. Natural Handwriting is one of the easiest ways to exchange information. In this world of technology exchanging information between user and computer is of immense importance and input devices such as keyboard and mouse have limitations as they cannot provide natural handwriting as input. Online handwriting recognition system can be used as an easiest and natural way of communication between user and human computers. Therefore Pen-Based interfaces are becoming more and more popular and hence a lot of research is being done for recognition. Research work presented in this thesis aims to recognize character with higher accuracy written in Gurumukhi script using Support Vector Machine (SVM) by improving processes of pre-processing phase used for recognition. Gurumukhi is a script of Punjabi Language which is widely spoken across the globe. This thesis is divided into five chapters. A brief outline of each chapter is given in the following paragraphs. First chapter of this thesis consists of introduction to online handwritten recognition system, issues in online handwritten recognition system overview of Gurmukhi script and literature review. Issues in online handwriting recognition system includes: handwriting style variations; constrained and unconstrained handwriting; personal, situational and material factors; writer dependent vs. writer independent recognition system. In literature review, a detailed literature survey on each phase of established procedure of online handwriting recognition has been presented. Second chapter gives the detailed work carried out in three phases. They are data collection, pre-processing and feature extraction. In data collection phase, input handwritten strokes are collected is shown. Phases of pre-processing are discussed and algorithms are presented. In the end feature extraction is explained. Third chapter describes the recognition techniques that can be used for online handwritten recognition system. In this work Support Vector Machine (SVM) is used as a classifier for the recognition. This chapter also illustrates the use of post-processing phase. Fourth Chapter contains the results of the algorithm proposed in this thesis work and a comparison is made with the proposed algorithms in Agrawal, (2012). The cross validation testing has been done for calculating the accuracy and 3, 4 and 5 fold cross validation testing is applied on a sample of 30, 50 and 70 of each zone. Finally, the result of the thesis is concluded in this chapter. Future scope of the work is also discussed.