An intelligent network for handwritten Devnagri digit recognition using Structural features

Abstract

This paper is concerned with recognition of handwritten Devnagri numerals. The basic objective of the present work is to provide an efficient and reliable technique for recognition of handwritten numerals. Automatic Recognition of Handwritten Devnagri Numerals is a difficult task. Handwritten Hindi Digit 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 Digit can be drawn in different sizes. Therefore, a robust offline Hindi handwritten recognition system has to account for all of these factors. In the proposed method each character is subjected to thinning followed by size normalization. The structural features viz. loop; end points, cross points, maximum profile distances, and special shapes like U, C and 180 tilted C are used for handwritten numerals recognition. Artificial Neural Network is used as classifier for recognition. It has numerous applications including those in postal sorting, bank cheque processing and form processing. In this work, an efficient method is proposed for recognition of isolated handwritten Devnagri numerals.