Efficient pre processing feature extraction and post processing algorithms for recognition of online handwritten gurmukhi script

Abstract

Human beings start recognizing digits, letters and symbols in very early stage of their childhood. This tendency of human beings to recognize alphabets/characters develops rapidly with the aging process. Though this natural process of recognizing alphabets or objects by human beings is taken for granted but the complexity of recognition system is realized when task of teaching the same is performed on a machine. Online Handwriting Recognition (OHWR) system is one such area of research that has gained world-wide attention of researchers. One main reason attributed to this popularity is that recognition of online handwriting has a wide range of applications at the interface between man and machine which ultimately facilitates easy way of giving inputs to the computer. Recognition of online handwritten character for any script is a difficult task due to problems posed by different handwriting styles of different writers, complexity of content and different handwriting devices used by user. Extensive research in the area of handwritten characters has made it feasible to recognize characters of English, Chinese, and Japanese language. However, less attention has been paid to recognition of Indian languages, especially, regional languages. A good number of methods have been proposed for recognition of handwritten English characters. However, the development of technologies is not at the same level for Indian scripts, especially Gurmukhi script which is complicated in terms of its structure and writing style. Hence, an attempt has been made in this thesis to provide online handwriting recognition system for Gurmukhi script. Main objective of this research was to propose efficient pre-processing, feature extraction and post-processing algorithms by introducing new constraints/ steps/ parameters for the recognition of online handwritten Gurmukhi script. Further, to validate the proposed algorithms targeting about 95% accuracy at character level.