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Proceedings Paper

Application of Fourier descriptors to the unconstrained handwritten digit recognition
Author(s): Yi Lu; Steven G. Schlosser; Michael Janeczko
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Paper Abstract

This paper presents the results of a comparative study of various Fourier descriptor representations and their use in the recognition of unconstrained handwritten digits. Certain characteristics of five Fourier descriptor representations of handwritten digits are discussed, and illustrations of ambiguous digit classes introduced by use of these Fourier descriptor representations are presented. It is concluded that Fourier descriptors are practically effective only within the framework of an intelligent system capable of reasoning about digit hypotheses. We describe a hypothesis-generating algorithm based on Fourier descriptors which allows the classifier to associate more than one possible digit class with each input. Such hypothesis-generating schemes can be very effective in systems employing multiple classifiers. We compare the performance of the five Fourier descriptor representations based on experiment results produced by the hypothesis-generating classifier for a test set of 14,000 handwritten digits. It is found that some Fourier descriptor formulations are more successful than others for handwritten digit recognition.

Paper Details

Date Published: 1 April 1992
PDF: 12 pages
Proc. SPIE 1623, The 20th AIPR Workshop: Computer Vision Applications: Meeting the Challenges, (1 April 1992);
Show Author Affiliations
Yi Lu, Environmental Research Institute of Michigan (United States)
Steven G. Schlosser, Environmental Research Institute of Michigan (United States)
Michael Janeczko, Environmental Research Institute of Michigan (United States)

Published in SPIE Proceedings Vol. 1623:
The 20th AIPR Workshop: Computer Vision Applications: Meeting the Challenges
Joan B. Lurie, Editor(s)

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