Prototyping structural description using an inductive learning program

1. INTRODUCTION Character recognition is commonly known as Optical Character Recognition ŽOCR. which deals with the recognition of optical characters. The origin of character recognition can be found as early as 1870 1 while it became a reality in the 1950s when the age of computer arrived.2 Commercial OCR machines and packages have been available since the mid 1950s. OCR has wide applications in modern society: document reading and sorting, postal address reading, bank cheque recognition, form recognition, signature verification, digital bar code reading, map interpretation, engineering drawing recognition, and various other industrial and commercial applications.3 11 The products that are currently commercially available for character recognition are limited to the recognition of typed text within a restricted number of fonts, or on-line recognition of hand-written characters. Products to perform off-line hand-printed text recognition are not available, although many approaches have been proposed. In fact there has recently been a high level of interest in applying machine learning to solve this problem.12 14 * Author to whom correspondence should be addressed; e-mail: amin@cse. unsw.edu.au INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, VOL. 15, 1103 1123 Ž2000. 2000 John Wiley & Sons, Inc. AMIN Much more difficult, and hence more interesting to researchers, is the ability to automatically recognize handwritten characters.15 The complexity of the problem is greatly increased by noise and by the wide variability of handwriting as a result of the mood of the writer and the nature of the writing. Analysis of cursive scripts requires the segmentation of characters within the word and the detection of individual features. This is not a problem unique to computers; even human beings, who possess the most efficient optical reading device Žeyes., have difficulty in...