A new feature extraction technique for on-line recognition of handwritten alphanumeric characters

Abstract

In this work a new method of feature extraction for an interactive and adaptive recognizer for on-line handwritten alphanumeric characters has been proposed. The system is suitable for use in conjunction with magnetic pen based devices for inputting data to a data processing system or a computer terminal. The features are extracted from dynamically changing locations of the writing device. The new method of feature extraction is simple, computationally light and fast enough for adaptive on-line use. Extracted features are robust with respect to all possible distortions like shape, size, and orientation. For simulation experiment, numerals 0–9 are used. A single hidden layer feed forward neural network trained by Quickprop algorithm, a variation of error back propagation is used for recognition. Very high recognition rates, even for highly distorted samples have been achieved confirming high generalization capability of the extracted feature set.