Training OCR Systems Using Variants of Ideal Images

This paper shows that the learning stage of highperformance character classifiers can be achieved using only ideal images and/or simple variants of them. We are interested in 94 character classes of the ASCII character set. We use a software tool to generate binary ideal images of these characters. Ideal images are supported with simple variants derived from them to represent character classes. Learning in a nearest neighbor classifier (type-A) is performed using ideal images of characters and their variants. Pixel intensity values are used as features. To judge on the effectiveness of this classifier, another novel nearest neighbor classifier (type-B) is built and trained using real images. These classifiers are tested using a real dataset. The overall recognition, error, and rejection rates of a threevariants type-A classifier are 98.5%, 1.5%, and 0.0%, respectively. The recognition rate of the type-B classifier exceeds that of type-A classifier that uses three variants by only 1.2%. Using other kinds of classifiers and using multiple classifier technology is expected to produce much impressive results for the type-A classifier. The type-A classifier that uses three variants requires 57 ms to recognize a character. The type-B classifier requires less than one half of the time required by type-A