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Reseach Article

Local Adaptive Automatic Binarisation (LAAB)

by T. Romen Singh, Sudipta Roy, Kh. Manglem Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 40 - Number 6
Year of Publication: 2012
Authors: T. Romen Singh, Sudipta Roy, Kh. Manglem Singh
10.5120/4961-7218

T. Romen Singh, Sudipta Roy, Kh. Manglem Singh . Local Adaptive Automatic Binarisation (LAAB). International Journal of Computer Applications. 40, 6 ( February 2012), 27-30. DOI=10.5120/4961-7218

@article{ 10.5120/4961-7218,
author = { T. Romen Singh, Sudipta Roy, Kh. Manglem Singh },
title = { Local Adaptive Automatic Binarisation (LAAB) },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 40 },
number = { 6 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 27-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume40/number6/4961-7218/ },
doi = { 10.5120/4961-7218 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:27:49.917912+05:30
%A T. Romen Singh
%A Sudipta Roy
%A Kh. Manglem Singh
%T Local Adaptive Automatic Binarisation (LAAB)
%J International Journal of Computer Applications
%@ 0975-8887
%V 40
%N 6
%P 27-30
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Most of the binarization techniques associate a certain intensity value called threshold which separate the pixel values of the concerned input grey scale image into two classes like background and foreground. Each and every pixel should be compared with the threshold and transformed to its respective class according to the threshold value. In this paper an automatic binarisation technique with local adaptation without any intensity value (threshold) of partition, is described. It creates a binarised image by transforming the input image to its respective binarised image automatically without using any threshold value. It uses local mean to adapt to local environment within a window of size WxW. Local mean determination is time consuming one and to reduce the time consumption, integral sum image is used as prior process. The input grey scale image is self transformed to an integral sum image within itself and then transform to binary image from the integral sum image itself.

References
  1. Konstantinos G. Derpanis,” Integral image-based representations”, Viola, P. & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In IEEE Computer Vision and Pattern Recognition (pp. I:511–518).
  2. N. Otsu, 1979 A threshold selection method from gray-level histograms, IEEE Trans. Systems, Man, and Cybernetics 9(1), pp.62-66.
  3. Bernsen, J. 1986, Dynamic thresholding of gray-level images. Proc. 8th Int. Conf. on Pattern Recognition, Paris, , pp. 1251–1255
  4. W. Niblack, 1986 An Introduction to Image Processing, Prentice-Hall, Englewood Cliffs, NJ,.
  5. Gorman, L.O. 1994, Binarization and multithresholding of document images using connectivity’, CVGIP, Graph. Models Image Process., 56, (6), pp. 494–506
  6. O.D. Trier and A. K. Jain, 1995. ‘‘Goal-directed evaluation of binarization methods,’’ IEEE Trans. Pattern Anal. Mach. Intell. PAMI-17, 1191– 1201
  7. Sauvola, J., Seppanen, T., Haapakoski, S., and Pietikainen, M.: ‘Adaptive document binarization’. Proc. 4th Int. Conf. on Document Analysis and Recognition, Ulm Germany, 1997, pp. 147–152
  8. Liu, Y., and Srihari, S.N.: ‘Document image binarization based on texture features’, IEEE Pattern Anal. Mach. Intell., 1997, 19, (5), pp. 540–544
  9. J. Sauvola and M. Pietikainen, “Adaptive document image binarization,” Pattern Recognition 33(2), pp. 225–236, 2000.
  10. M. Sezgin and B. Sankur, ‘‘Comparison of thresholding methods for non-destructive testing applications,’’ IEEE ICIP’2001, Intl. Conf. Image Process., pp. 764–767 (2001).
  11. P. Viola and M. J. Jones, 2004 “Robust real-time face detection,” Int. Journal of Computer Vision 57(2), pp. 137– 154,
  12. Mehmet Sezgin and Bu¨ lent Sankur, 2004 “Survey over image thresholding techniques and quantitative performance evaluation”, Journal of Electronic Imaging 13(1), 146–165 (January).
  13. F. Shafait, D. Keysers, and T. M. Breuel, “Performance comparison of six algorithms for page segmentation,” in 7th IAPR Workshop on Document Analysis Systems, pp. 368–379, (Nelson, New Zealand), Feb. 2006.
  14. B. Gatos, I. Pratikakis and S.J. Perantonis,’ Improved Document Image Binarization by Using a Combination of Multiple Binarization Techniques and Adapted Edge Information’, 978-1-4244-2175-6/08/$25.00 ©2008 IEEE.
  15. B. Su, S. Lu, and C. L. Tan, “Document Image Binarization Using Background Estimation and Stroke Edges,” Proc. Intl. Journal on Document Analysis &Recognition, Vol. 13, No. 4, pp. 303-314, 2010.
  16. Ioannis Pratikakis, Basilis Gatos and Konstantinos Ntirogiannis, “H-DIBCO 2010 –Handwritten Document Image Binarization Competition”, 2010 12th International Conference on Frontiers in Handwriting Recognition. 978-0-7695-4221-8/10 $26.00 © 2010 IEEEDOI 10.1109/ICFHR.2010.118.
  17. I. Ben Messaoud, H. Amiri, H. El Abed, and V.Märgner, “New binarization approach based on text blockextraction”, in International Conference on Document Analysis and Recognition (ICDAR), 2011.
  18. T.Obafemi-Ajayi and G. Agam. “Statistical multiresolution schemes for historical document binarization”. In Document Recognition and Retrieval XVIII, Proc. SPIE,2011.
  19. F. Kleber, M. Diem and R. Sablatnig. “Scale Space Binarization Using Edge Information Weighted by a Foreground Estimation”, ICDAR 2011.
  20. MA. Ramírez-Ortegón, E. Dueñez-Guzmán, R. Rojas, and E. Cuevas, “Unsupervised Evaluation Measures for Binarization”, Pattern Recognition, Vol. 44, issue 3, pp 491-502, 2011.
  21. Ioannis Pratikakis, Basilis Gatos and Konstantinos Ntirogiannis,”ICDAR 2011 Document Image Binarization Contest (DIBCO 2011)”, 2011 International Conference on Document Analysis and Recognition, 1520-5363/11 $26.00 © 2011 IEEE DOI 10.1109/ICDAR.2011.299,pp 1506-1510.
  22. T.Romen Singh, Sudipta Roy, O.Imocha Singh, Tejmani Sinam and Kh.Manglem Singh,” A New local Adaptive Thresholding Technique in Binarisation”, IJCSI-Vol 8, issue 6 No. 2 pp. 271-277 (Nov, 2011).
  23. Yudong ZHANG , Lenan WU†,” Fast Document Image Binarization Based on an Improved Adaptive Otsu’s Method and Destination Word Accumulation”, Journal of Computational Information Systems 7: 6 (2011) 1886-1892, 1553-9105/ Copyright © 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Automatic Binarisation local adaptive integral sum image autobinarization