An efficient method for the Devnagri handwritten vowel recognition

Abstract

Development of a Character recognition system for Devnagri is difficult because (i) there are basic, modified ("matra") and compound character shapes in the script and (ii) the characters in words are topologically connected. Here focus is on the recognition of offline handwritten Hindi vowels that can be used in common applications like commercial forms, government records, bill processing systems, Signature verification, passport readers. Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Challenges in handwritten characters recognition lie in the variation and distortion of offline handwritten characters since different people may use different style of handwriting, and direction to draw the same shape of any Hindi character. This paper describes the nature of handwritten language, how it is translated into electronic data, and the basic concepts behind written language recognition algorithms. Handwritten Hindi character unlike the printed character, are imprecise in nature as their corners are not always sharp, lines are not perfectly straight, and curves are not necessarily smooth. Therefore, a robust offline Hindi handwritten recognition system has to account for all of these factors. In this 14 class classification problem a new feature set made up of horizontal and vertical histogram is introduced. Support Vector Machine is used as classifier for recognition of Handwritten Hindi vowels.