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Reseach Article

Enhancement of Degraded Historical Kannada Documents

by B Gangamma, Srikanta Murthy K
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 29 - Number 11
Year of Publication: 2011
Authors: B Gangamma, Srikanta Murthy K
10.5120/3692-5155

B Gangamma, Srikanta Murthy K . Enhancement of Degraded Historical Kannada Documents. International Journal of Computer Applications. 29, 11 ( September 2011), 1-6. DOI=10.5120/3692-5155

@article{ 10.5120/3692-5155,
author = { B Gangamma, Srikanta Murthy K },
title = { Enhancement of Degraded Historical Kannada Documents },
journal = { International Journal of Computer Applications },
issue_date = { September 2011 },
volume = { 29 },
number = { 11 },
month = { September },
year = { 2011 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume29/number11/3692-5155/ },
doi = { 10.5120/3692-5155 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:15:30.529561+05:30
%A B Gangamma
%A Srikanta Murthy K
%T Enhancement of Degraded Historical Kannada Documents
%J International Journal of Computer Applications
%@ 0975-8887
%V 29
%N 11
%P 1-6
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Historical documents play a vital role in understanding our past and hence need to be preserved. Over the period, these documents tend to possess many variations like stains, strain, ink seepage, dust etc. Image enhancement techniques can be utilized to improve the quality of these images by removing noise and increasing contrast range. The proposed method mainly deals with enhancing the historical document image of palm scripts using gray scale morphological operations with the combination of spatial filters. Morphological opening operation is applied to compensate for non uniform background intensity and suppress bright details smaller than the structural element, while closing suppresses the dark details. The proposed method works well for the images with dark background and low contrast and enhances the image with clear white background.

References
  1. Drira Fadoua, Frank Le Bourgeis and Hubert Emptoz, “Restoring Ink Bleed Through Degraded Document Images Using a Recursive Unsupervised Classification Technique”. Spinger-Verlag Berlin Heidelberg, DAS LNCS 3872, pp. 38-49.
  2. B. Gatos, I. Pratikakis, S.J. Perantonis, “Adaptive Degraded Document Image Binarization”, Journal of Pattern Recognition 39, 317 – 327.
  3. N. Krishna Kishore, Priti P. Rege, “Adaptive Enhancement of Historical Document Images”, IEEE International Symposium on Signal Processing and Information Technology, 978-1 -4244-1 835-0/07, pp 983-88.
  4. B. Gatos, I. Pratikakis and S.J. Perantonis, “Improved Document Image Binarization by Using a Combination of Multiple Binarization Techniques and Adapted Edge Information”, 19th International Conference on Pattern Recognition (ICPR'08), ISBN: 978-1-4244-2175-6/08.
  5. Yahia S. Halabi, Zaid SA, “Modeling Adaptive Degraded Document Image Binarization and Optical Character System”, European Journal of Scientific Research , ISSN 1450-216X Vol.28 No.1 2009, pp.14-32.
  6. Xiangyun Ye, Mohamed Cheriet, Ching Y. Suen and Ke Liu,, “Extraction of bank check items by mathematical morphology”, International journal on Document Analysis and Recognition, SpringerLink, Volume 2, pp 53-66.
  7. Santosh Shetty and M Shridhar, “Background elimination in bank cheques using gray scale morphology”, Proceedings of the Seventh International Workshop on Frontiers in Handwriting Recognition, Amsterdam, ISBN 90-76942-01-3, pp 83-92.
  8. Ferhat Fillali, Khier Benmahammed and Graini Abid, “Image restoration using SVD and adaptive regularization” J. Automation & Systems Engineering 4-3 pp. 173-181.
  9. Frank Shih, “Image Processing and Mathematical Morphology Fundamentals and Applications”, Wiley publications, IEEE press.
  10. Rafael C Gonzalez and Richard E Woods, “Digital Image processing”, Third Edition, PHI publication, 2008..
  11. Nobuyuki Otsu (1979) “A threshold selection method from gray level histograms” IEEE Trans. Systems Man and Cybernetics, Volume 9, Issue 1, 1979 pp:62-66.
  12. HIPR2, available at www.homepages.inf.ed.ac.uk/rbf/HIPR2/adpthrsh
Index Terms

Computer Science
Information Sciences

Keywords

Adaptive Histogram Equalization Degraded Documents Enhancement Gray Scale Morphological Operators Inscriptions