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Proceedings Paper

Morphological approach to machine-printed character recognition: a feasibility study
Author(s): Radovan V. Krtolica; Brian Warner
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Paper Abstract

A connected skeleton is obtained from the image of a character on a rectangular grid with binary values, by use of discrete morphological processing with respect to a set of structuring elements. The skeletonizing procedure is based on sequential thinning, a well known morphological operation involving multiple use of the hit-miss transform. However, the sequence of thinning operations is carefully chosen to provide for robustness of resulting skeleton and its characteristic points (intersections and extremes) that are identified subsequently. Connectivity graphs of intersections and extremes of a character image are an affine invariant feature useful for character recognition. Ambiguities in character classification based on this feature are due to the fact that the graph adjacency (connectivity) matrix does not tell the difference between characteristic points connected by a stroke representing a straight line and a stroke representing a curve. An orthogonal fitting technique is proposed that discriminates between curved and straight strokes. Straight lines are then represented by graph edges, while the curves are replaced by a few additional mutually connected graph vertices. Experimental results show good discrimination properties of the extended connectivity graphs on 12 points Courier font characters.

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: 10.1117/12.130279
Show Author Affiliations
Radovan V. Krtolica, Canon Research Ctr. America, Inc. (United States)
Brian Warner, Canon Research Ctr. America, Inc. (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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