1 April 1993 Morphological hand-printed character recognition by a skeleton-matching algorithm
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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 skeleton-matching 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 handprinted 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 skeleton-based approach and the inadequacy of the pattern-spectrum-based approach.
Panagiotis E. Trahanias, Panagiotis E. Trahanias, Konstantinos Stathatos, Konstantinos Stathatos, Fotios Stamatelopoulos, Fotios Stamatelopoulos, Emmanuel Skordalakis, Emmanuel Skordalakis, } "Morphological hand-printed character recognition by a skeleton-matching algorithm," Journal of Electronic Imaging 2(2), (1 April 1993). https://doi.org/10.1117/12.143731 . Submission:
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