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Proceedings Paper

Automated optical recognition of degraded handwritten characters
Author(s): Emade Darwiche; Abhijit S. Pandya; Anil D. Mandalia
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Paper Abstract

This paper reports on a new approach in the field of automated optical recognition of handwritten characters. The approach combines geometrical and topological features, distribution of points, and Alopex based neural network to achieve a high recognition rate. A considerable enhancement in speed is achieved by implementing the process on a compressed image. Distortion tolerant features along with noise removal and region merging permit the handling of degraded documents and characters. Software implementation of the system experimented on the NIST database yields to a recognition rate of 92.4 for numerals and upper-case letters.

Paper Details

Date Published: 1 August 1992
PDF: 12 pages
Proc. SPIE 1661, Machine Vision Applications in Character Recognition and Industrial Inspection, (1 August 1992); doi: 10.1117/12.130288
Show Author Affiliations
Emade Darwiche, Florida Atlantic Univ. (United States)
Abhijit S. Pandya, Florida Atlantic Univ. (United States)
Anil D. Mandalia, Florida Atlantic Univ. (United States)


Published in SPIE Proceedings Vol. 1661:
Machine Vision Applications in Character Recognition and Industrial Inspection
Donald P. D'Amato; Wolf-Ekkehard Blanz; Byron E. Dom; Sargur N. Srihari, Editor(s)

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