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Journal of Electronic Imaging

Morphological hand-printed character recognition by a skeleton-matching algorithm
Author(s): Panagiotis E. Trahanias; Konstantinos Stathatos; Fotios Stamatelopoulos; Emmanuel Skordalakis
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Paper Abstract

We study the use of mathematical morphology for handprinted character recognition. Our approach uses the morphological skeleton transform as the shape descriptor. An efficient skeletonmatching algorithm, which renders the similarity between two skeletons as a distance measure, is employed. Based on this distance measure, a character is classified by a minimum distance classifier. The morphological skeleton transform contains complete shape information and is shown as a powerful descriptor for this class of shapes. We also study the pattern spectrum as a shape descriptor for hand-printed characters. However, the pattern spectrum conveys only information about the shape/size distribution of a given object, which turns out to be not very efficient for hand-printed characters. Experimental results demonstrate the efficiency of the skeletonbased approach and the inadequacy of the pattern-spectrum-based approach.

Paper Details

Date Published: 1 April 1993
PDF: 12 pages
J. Electron. Imag. 2(2) doi: 10.1117/12.143731
Published in: Journal of Electronic Imaging Volume 2, Issue 2
Show Author Affiliations
Panagiotis E. Trahanias, Univ. of Toronto (Canada)
Konstantinos Stathatos, Univ. of Maryland/College Park (United States)
Fotios Stamatelopoulos, Univ. of Maryland/College Park (United States)
Emmanuel Skordalakis, National Technical Univ. of Athens (Greece)


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