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Proceedings Paper

Box connectivity approach to multifont character recognition
Author(s): Radovan V. Krtolica
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Paper Abstract

The idea of box connectivity approach (BCA) is to partition the bounding frame of the character bitmap into a fixed number of rectangles, to define some properties of those rectangles, and to establish connectivity relations between the rectangles. Hamming distance and vector optimization are used for classification. Good results in recognition of high quality data (400 dpi) in three fonts (Courier, Helvetica, and New Times Roman) and eight sizes (from 8 to 24 points) were reported in a previous paper. These findings are confirmed in this paper by an experiment showing that, for the same number of bits, BCA features increase the rate of recognition twice with respect to features obtained by simple decimation. However, the actual method is limited by the fact that the number of referent templates increases with the number of fonts to be recognized. The purpose of this paper is to remove this limitation. The main part of the paper discusses the properties of the Hamming distance and how they can be used in the BCA algorithm to improve the efficiency of classification. The last section reports the results of an experiment showing the discrimination power of BCA features.

Paper Details

Date Published: 23 March 1994
PDF: 7 pages
Proc. SPIE 2181, Document Recognition, (23 March 1994); doi: 10.1117/12.171119
Show Author Affiliations
Radovan V. Krtolica, Canon Research Ctr. America, Inc. (United States)

Published in SPIE Proceedings Vol. 2181:
Document Recognition
Luc M. Vincent; Theo Pavlidis, Editor(s)

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