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Proceedings Paper

Omnifont Character Recognition Based On Fast Feature Vectorization
Author(s): H. C. Yung; I. M. Green
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Paper Abstract

This paper presents major achievements made towards the development of a high-speed optical character recognition (OCR) workstation for characters of various fonts and sizes. The system is based upon an efficient feature extraction concept centred around an edge-vectorization technique. The resulting edges are mapped into a feature space from where a binary feature vector is built and subsequently fed to a standard statistical Bayesian classifier. The technique has been demonstrated on an IBM-PC/XT (without coprocessor) to operate at least 25 times the speed of conventional OCR techniques, achieving a 100% recognition rate with learned characters and 87% with unlearned.

Paper Details

Date Published: 25 October 1988
PDF: 9 pages
Proc. SPIE 1001, Visual Communications and Image Processing '88: Third in a Series, (25 October 1988); doi: 10.1117/12.969007
Show Author Affiliations
H. C. Yung, University of Newcastle upon Tyne (United Kingdom)
I. M. Green, University of Newcastle upon Tyne (United Kingdom)


Published in SPIE Proceedings Vol. 1001:
Visual Communications and Image Processing '88: Third in a Series
T. Russell Hsing, Editor(s)

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