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Foreground Estimation in a Degraded Text document

by I Bhuvana Chandra, K Nagarjunavarma, Gireesh Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 44 - Number 22
Year of Publication: 2012
Authors: I Bhuvana Chandra, K Nagarjunavarma, Gireesh Kumar
10.5120/6413-8852

I Bhuvana Chandra, K Nagarjunavarma, Gireesh Kumar . Foreground Estimation in a Degraded Text document. International Journal of Computer Applications. 44, 22 ( April 2012), 31-37. DOI=10.5120/6413-8852

@article{ 10.5120/6413-8852,
author = { I Bhuvana Chandra, K Nagarjunavarma, Gireesh Kumar },
title = { Foreground Estimation in a Degraded Text document },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 44 },
number = { 22 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 31-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume44/number22/6413-8852/ },
doi = { 10.5120/6413-8852 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:36:15.213939+05:30
%A I Bhuvana Chandra
%A K Nagarjunavarma
%A Gireesh Kumar
%T Foreground Estimation in a Degraded Text document
%J International Journal of Computer Applications
%@ 0975-8887
%V 44
%N 22
%P 31-37
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper an attempt is made to retrieve the text region alone from a degraded text document. For doing that, four different filters are used for noise removal in the text document. Later document binarization is done using thresholding. Three different thresholding techniques are implemented for foreground-background separation. Then candidate region is selected and features are extracted. The features are then fed to an SVM to classify text and non-text regions. The proposed approach is implemented and tested on various hand written and machine printed degraded text documents.

References
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  2. Sauvola J. , M. Pietikainen, "Adaptive Document Image Binarization", Pattern Recognition 33 (2000).
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  5. B. Gatos, I. Pratikakis, S. J. Perantonis, Adaptive degraded document image binarization, The journal of the pattern recognition society, pg. 4,5
  6. AbhijitMitra, Restoration of Noisy Document Images with an Efficient Bi-Level Adaptive Thresholding, International Journal of Computational Intelligence Volume 2 Number 2
  7. T Kasar, J Kumar and A G Ramakrishnan, Font andBackground Color Independent Text Binarization
  8. K. S. Sim *, C. P. Tso, Y. Y. Tan, Recursive sub-image histogram equalization applied to gray scale images
Index Terms

Computer Science
Information Sciences

Keywords

Image Filtering Wavelet Decomposition Feature Extraction Document Binarization

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