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Proceedings Paper

Extracting curved text lines using the chain composition and the expanded grouping method
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Paper Abstract

In this paper, we present a method to extract the text lines in poorly structured documents. The text lines may have different orientations, considerably curved shapes, and there are possibly a few wide inter-word gaps in a text line. Those text lines can be found in posters, blocks of addresses, artistic documents. Our method is an expansion of the traditional perceptual grouping. We develop novel solutions to overcome the problems of insufficient seed points and varied orientations in a single line. In this paper, we assume that text lines consists of connected components, in which each connected components is a set of black pixels within a letter, or some touched letters. In our scheme, the connected components closer than an iteratively incremented threshold will be combined to make chains of connected components. Elongate chains are identified as the seed chains of lines. Then the seed chains are extended to the left and the right regarding the local orientations. The local orientations will be reevaluated at each side of the chains when it is extended. By this process, all text lines are finally constructed. The advantage of the proposed method over prior works in extraction of curved text lines is that this method can both deal with more than a specific language and extract text lines containing some wide inter-word gaps. The proposed method is good for extraction of the considerably curved text lines from logos and slogans in our experiment; 98% and 94% for the straight-line extraction and the curved-line extraction, respectively.

Paper Details

Date Published: 28 January 2008
PDF: 9 pages
Proc. SPIE 6815, Document Recognition and Retrieval XV, 68150U (28 January 2008); doi: 10.1117/12.766057
Show Author Affiliations
Nguyen Noi Bai, Chungbuk National Univ. (South Korea)
Kim Nam, Chungbuk National Univ. (South Korea)
Youngjun Song, Chungbuk BIT Research-Oriented Univ. Consortium (South Korea)

Published in SPIE Proceedings Vol. 6815:
Document Recognition and Retrieval XV
Berrin A. Yanikoglu; Kathrin Berkner, Editor(s)

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