This thesis investigates a character recognition method inspired by the premise that humans recognize shapes using their ability to assimilate a set of primitive features. These features collectively create a higher level shape of a certain category. The primitive features employed in our method include horizontal, vertical, diagonal lines, and corners of various orientations positioned at various places within a character. Combinations of these features form categories of characters to be recognized. The basic approach consists of preprocessing a character bitmap, extracting primitive features to form a feature vector. The feature vector is then input to a classification neural net. Based on weights derived during training, the system selects the character most closely identified by the feature vector. The advantages of this approach are the speed of training and recognition (as opposed to methods which continually iterate to the final solution), and robustness of the "blurring" effect realized by transforming a character bitmap to an array of features, rather than attempting template matching at the bitmap or pixel level. To support this study, a graphics workstation based environment has been developed, equiped with 3000 16X16 pixel characters, suitable for experimentation.

Library of Congress Subject Headings

Optical character recognition devices; Pattern perception; Neural networks (Computer science); Pattern recognition systems

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Computer Science (GCCIS)


Not Listed


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