Adaptive nonlinear shape matching for unconstrained handwritten character recognition
Identifieur interne : 002233 ( Istex/Corpus ); précédent : 002232; suivant : 002234Adaptive nonlinear shape matching for unconstrained handwritten character recognition
Auteurs : Jeong-Seon Park ; Seong-Whan LeeSource :
- Pattern Recognition [ 0031-3203 ] ; 1995.
Abstract
In this paper, we propose an adaptive nonlinear shape matching method which can compensate for the various distortions in unconstrained handwritten characters. In the proposed method, structural information is incorporated to improve the accuracy of matching, and only neighboring pixels of each black pixel are considered to reduce the computational complexity of single matching procedure. Also, iterative nonlinear shape matching procedures in each subregion are adaptively accomplished according to the results of that subregion, in order to accelerate the convergence speed of the matching procedure. In order to verify the performance of the proposed method, experiments with large-set unconstrained handwritten Hangul character database PE92 have been performed. Experimental results reveal that the proposed method is superior to the previous nonlinear shape matching method in processing speed and accuracy of matching.
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DOI: 10.1016/0031-3203(95)00008-N
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<front><div type="abstract" xml:lang="en">In this paper, we propose an adaptive nonlinear shape matching method which can compensate for the various distortions in unconstrained handwritten characters. In the proposed method, structural information is incorporated to improve the accuracy of matching, and only neighboring pixels of each black pixel are considered to reduce the computational complexity of single matching procedure. Also, iterative nonlinear shape matching procedures in each subregion are adaptively accomplished according to the results of that subregion, in order to accelerate the convergence speed of the matching procedure. In order to verify the performance of the proposed method, experiments with large-set unconstrained handwritten Hangul character database PE92 have been performed. Experimental results reveal that the proposed method is superior to the previous nonlinear shape matching method in processing speed and accuracy of matching.</div>
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<abstract xml:lang="en"><p>In this paper, we propose an adaptive nonlinear shape matching method which can compensate for the various distortions in unconstrained handwritten characters. In the proposed method, structural information is incorporated to improve the accuracy of matching, and only neighboring pixels of each black pixel are considered to reduce the computational complexity of single matching procedure. Also, iterative nonlinear shape matching procedures in each subregion are adaptively accomplished according to the results of that subregion, in order to accelerate the convergence speed of the matching procedure. In order to verify the performance of the proposed method, experiments with large-set unconstrained handwritten Hangul character database PE92 have been performed. Experimental results reveal that the proposed method is superior to the previous nonlinear shape matching method in processing speed and accuracy of matching.</p>
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<abstract lang="en">In this paper, we propose an adaptive nonlinear shape matching method which can compensate for the various distortions in unconstrained handwritten characters. In the proposed method, structural information is incorporated to improve the accuracy of matching, and only neighboring pixels of each black pixel are considered to reduce the computational complexity of single matching procedure. Also, iterative nonlinear shape matching procedures in each subregion are adaptively accomplished according to the results of that subregion, in order to accelerate the convergence speed of the matching procedure. In order to verify the performance of the proposed method, experiments with large-set unconstrained handwritten Hangul character database PE92 have been performed. Experimental results reveal that the proposed method is superior to the previous nonlinear shape matching method in processing speed and accuracy of matching.</abstract>
<note>A preliminary version of this paper has been presented at the 4th International Workshop on Frontiers in Handwriting Recognition, Taipei, Taiwan, (December 1994).</note>
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