Morphological approach to machine-printed character recognition: a feasibility study

Radovan V. Krtolica; Brian Warner

doi:10.1117/12.130279

1 August 1992 Morphological approach to machine-printed character recognition: a feasibility study

Radovan V. Krtolica, Brian Warner

Proceedings Volume 1661, Machine Vision Applications in Character Recognition and Industrial Inspection; (1992) https://doi.org/10.1117/12.130279
Event: SPIE/IS&T 1992 Symposium on Electronic Imaging: Science and Technology, 1992, San Jose, CA, United States

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.

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Radovan V. Krtolica and Brian Warner "Morphological approach to machine-printed character recognition: a feasibility study", Proc. SPIE 1661, Machine Vision Applications in Character Recognition and Industrial Inspection, (1 August 1992); https://doi.org/10.1117/12.130279

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