Recognition of Handwritten Devanagari Script Using Soft Computing

Abstract

Development of a Character recognition system for Devnagri is difficult because (i) there are about 350 basic, modified (“matra”) and compound character shapes in the script and (ii) the characters in a words are topologically connected. Here focus is on the recognition of offline handwritten Hindi characters that can be used in common applications like bank cheques, commercial forms, government records, bill processing systems, Postcode Recognition, Signature Verification, passport readers, offline document recognition generated by the expanding technological society. 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 Hindi characters since different people may use different style of handwriting, and direction to draw the same shape of any Hindi character. This overview 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 are imprecise in nature as their corners are not always sharp, lines are not perfectly straight, and curves are not necessarily smooth, unlikely the printed character. Furthermore, Hindi character can be drawn in different sizes and orientation in contrast to handwriting which is often assumed to be written on a baseline in an upright position. Therefore, a robust offline Hindi handwritten recognition system has to account for all of these factors. An approach using Artificial Neural Network is considered for recognition of Handwritten Hindi Character Recognition.