Efficient Off-Line Cursive Handwriting Word Recognition

Abstract : In this paper, we present an off-line cursive word handwriting recognition methodology. This is based on a novel combination of two different modes of word image normalization and robust hybrid feature extraction. Word image normalization is performed by using as a reference point the geometric center of the word image as well as by placing the baseline of the word in the center of a rectangular box. Additionally, image pre-processing is performed in order to correct word skew, word slant as well as to normalize the stroke thickness. At a next step, two types of features are combined in a hybrid fashion. The first one, divides the word image into a set of zones and calculates the density of the character pixels in each zone. In the second type of features, we calculate the area that is formed from the projections of the upper and lower profile of the word. The performance of the proposed methodology is demonstrated after testing with the reference IAM cursive handwriting database.
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Communication dans un congrès
Guy Lorette. Tenth International Workshop on Frontiers in Handwriting Recognition, Oct 2006, La Baule (France), Suvisoft, 2006
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Dernière modification le : vendredi 6 octobre 2006 - 11:39:09
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  • HAL Id : inria-00104302, version 1

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B. Gatos, I. Pratikakis, A.L. Kesidis, S.J. Perantonis. Efficient Off-Line Cursive Handwriting Word Recognition. Guy Lorette. Tenth International Workshop on Frontiers in Handwriting Recognition, Oct 2006, La Baule (France), Suvisoft, 2006. 〈inria-00104302〉

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