optimization of the gaussian kernel extended by binary morphology for text line segmentation

Clicks: 91
ID: 167950
2010
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Ranked #81 of 100 articles by views in molecular therapy : the journal of the american society of gene therapy

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Abstract
In this paper, an approach for text line segmentation by algorithm with the implementation of the Gaussian kernel is presented. As a result of algorithm, the growing area around text is exploited for text line segmentation. To improve text line segmentation process, isotropic Gaussian kernel is extended by dilatation. Furthermore, algorithms with isotropic and extended Gaussian kernels are examined and evaluated under different text samples. Results are given and comparative analysis is made for these algorithms. From the obtained results, optimization of the parameters defining extended Gaussian kernel dimension is proposed. The presented algorithm with the extended Gaussian kernel showed robustness for different types of text samples.
Reference Key
milivojevic2010radioengineeringoptimization Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Z. Milivojevic;D. Brodic
Journal molecular therapy : the journal of the american society of gene therapy
Year 2010
DOI
DOI not found
URL
www.radioeng.cz/fulltexts/2010/10_04_718_724.pdf
Keywords
electronics nuclear engineering morphological operationelectrical engineering

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