CFP last date
20 May 2024
Reseach Article

Local Contrast and Mean Thresholding in Image Binarization

by O. Imocha Singh, Tejmani Sinam, O. James, T. Romen Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 51 - Number 6
Year of Publication: 2012
Authors: O. Imocha Singh, Tejmani Sinam, O. James, T. Romen Singh
10.5120/8044-1362

O. Imocha Singh, Tejmani Sinam, O. James, T. Romen Singh . Local Contrast and Mean Thresholding in Image Binarization. International Journal of Computer Applications. 51, 6 ( August 2012), 4-10. DOI=10.5120/8044-1362

@article{ 10.5120/8044-1362,
author = { O. Imocha Singh, Tejmani Sinam, O. James, T. Romen Singh },
title = { Local Contrast and Mean Thresholding in Image Binarization },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 51 },
number = { 6 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 4-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume51/number6/8044-1362/ },
doi = { 10.5120/8044-1362 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:49:40.563687+05:30
%A O. Imocha Singh
%A Tejmani Sinam
%A O. James
%A T. Romen Singh
%T Local Contrast and Mean Thresholding in Image Binarization
%J International Journal of Computer Applications
%@ 0975-8887
%V 51
%N 6
%P 4-10
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Binarization is a process of separation of pixel values of an input image into two pixel values like white as background and black as foreground. It is an important part in image processing and it is the first step in many document analysis and OCR processes. Most of the binarization techniques associate a certain intensity value called threshold which separate the pixel values of the concerned input grayscale 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. Thus threshold takes a major role in binarization. Hence determination of proper threshold value in binarization is a major factor of being a good binarised image and it can be approached in two categories like global thresholding and local thresholding techniques. In uniform contrast distribution of background and foreground documents, global thesholding is more suitable than that of local thresholding one. In degraded documents, where considerable background noise or variation in contrast and illumination exists, local technique is more suitable than that of global one. In this paper a local thresholding technique using local contrast and mean is described. Local adaptation is carried out with the local contrast and mean.

References
  1. N. Otsu, "A threshold selection method from gray-level histograms," IEEE Trans. Systems, Man, and Cybernetics 9(1), pp. 62-66 , 1979.
  2. M. Kamel and A. Zhao, ''Extraction of binary character/graphics images from grayscale document images,'' Graph. Models Image Process. 55(3), 203–217(1993).
  3. T. Abak, U. Baris¸, and B. Sankur, ''The performance of thresholding algorithms for optical character recognition,'' Intl. Conf. Document Anal. Recog. ICDAR'97, pp. 697–700 (1997).
  4. O. D. Trier and A. K. Jain, ''Goal-directed evaluation of binarization methods,'' IEEE Trans. Pattern Anal. Mach. Intell. PAMI-17, 1191– 1201(1995).
  5. B. Bhanu, ''Automatic target recognition: state of the art survey,'' IEEE Trans. Aerosp. Electron. Syst. AES-22, 364–379 (1986).
  6. M. Sezgin and R. Tasaltin, ''A new dichotomization technique to multilevel thresholding devoted to inspection applications,'' Pattern Recogn. Lett. 21, 151–161 (2000).
  7. 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).
  8. J. Sauvola and M. Pietikainen, "Adaptive document image binarization," Pattern Recognition 33(2), pp. 225–236, 2000.
  9. P. Viola and M. J. Jones, "Robust real-time face detection," Int. Journal of Computer Vision 57(2), pp. 137– 154, 2004.
  10. R. Cattoni, T. Coianiz, S. Messelodi, and C. M. Modena, "Geometric layout analysis techniques for document image understanding: a review," tech. rep. , IRST, Trento, Italy, 1998.
  11. 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.
  12. J. M. White and G. D. Rohrer, "Image thresholding for optical character recognition and other applications requiring character image extraction," IBM Journal of Research and Development 27, pp. 400–411, July 1983.
  13. Bernsen, J. : 'Dynamic thresholding of gray-level images'. Proc. 8th Int. Conf. on Pattern Recognition, Paris, 1986, pp. 1251–1255
  14. Chow, C. K. , and Kaneko, T. : 'Automatic detection of the left ventricle from cineangiograms', Comput. Biomed. Res. , 1972, 5, pp. 388–410
  15. Eikvil, L. , Taxt, T. , and Moen, K. : 'A fast adaptive method for binarization of document images'. Proc. ICDAR, France, 1991, pp. 435–443
  16. Mardia, K. V. , and Hainsworth, T. J. : 'A spatial thresholding method for image segmentation', IEEE Trans. Pattern Anal. Mach. Intell. , 1988, 10, (8), pp. 919–927
  17. Niblack, W. : 'An introduction to digital image processing' (Prentice- Hall, Englewood Cliffs, NJ, 1986), pp. 115–116
  18. Taxt, T. , Flynn, P. J. , and Jain, A. K. : 'Segmentation of document images', IEEE Trans. Pattern Anal. Mach. Intell. , 1989, 11, (12), pp. 1322–1329
  19. Yanowitz, S. D. , and Bruckstein, A. M. : 'A new method for image segmentation', Comput. Vis. Graph. Image Process. , 1989, 46, (1), pp. 82–95
  20. T. Romen Singh, Sudipta Roy, O. Imocha Singh, Tejmani Sinam and Kh. Manglem Singh," A New local Adaptive Thresholding Technique in Binarization", IJCSI-Vol 8, issue 6 No. 2 pp. 271-277 (Nov, 2011).
  21. Liu, Y. , and Srihari, S. N. : 'Document image binarization based on texture features', IEEE Pattern Anal. Mach. Intell. , 1997, 19, (5), pp. 540–544
  22. L. O'Gorman, "Binarization and multithresholding of document images using connectivity," Graphical Model and Image Processing 56, pp. 494–506, Nov. 1994.
  23. K. Sobottka, H. Kronenberg, T. Perroud, and H. Bunke, "Text extraction from colored book and journal covers," Int. Jour. on Document Analysis and Recognition 2, pp. 163–176, June 2000.
  24. C. M. Tsai and H. J. Lee, "Binarization of color document images via luminance and saturation color features," IEEE Trans. on Image Processing 11, pp. 434–451, April 2002.
  25. E. Badekas, N. Nikolaou, and N. Papamarkos, "Text binarization in color documents," Int. Jour. of Imaging Systems and Technology 16(6), pp. 262–274, 2006.
  26. F. Shafait, J. van Beusekom, D. Keysers, and T. M. Breuel, "Page frame detection for marginal noise removal from scanned documents," in 15th Scandinavian Conference on Image Analysis, pp. 651–660, (Aalborg, Denmark), June 2007.
  27. P. Viola and M. J. Jones, "Robust real-time face detection," Int. Journal of Computer Vision 57(2), pp. 137– 154, 2004.
  28. O. D. Trier and T. Taxt, "Evaluation of binarization methods for document images," IEEE Trans. On Pattern Analysis and Machine Intelligence 17, pp. 312–315, March 1995
  29. F. C. Crow, "Summed-area tables for texture mapping," Computer Graphics - Proceedings of SIGGRAPH' 84 18(3), pp. 207–212, 1984.
  30. Y. Nakagawa and A. Rosenfeld, ''Some experiments on variable thresholding,'' Pattern Recogn. 11~3!, 191–204 ~1979!.
  31. F. Deravi and S. K. Pal, ''Gray level thresholding using second-order statistics,'' Pattern Recogn. Lett. 1, 417–422 ~1983!.
  32. W. A. Yasnoff, J. K. Mui, and J. W. Bacus, ''Error measures for scene segmentation,'' Pattern Recogn. 9, 217–231 (1977).
  33. 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).
  34. M. D. Levine and A. M. Nazif, ''Dynamic measurement of computer generated image segmentations,'' IEEE Trans. Pattern Anal. Mach. Intell. PAMI-7, 155–164 (1985).
  35. Y. J. Zhang, ''A survey on evaluation methods for image segmentation,'' Pattern Recogn. 29, 1335–1346 (1996).
  36. E. Badekas and N. Papamarkos, "Document binarization using Kohonen SOM", IET Image Process. , Vol. 1, No. 1, March 2007
  37. Mehmet Sezgin and Bu¨ lent Sankur "Survey over image thresholding techniques and quantitative performance evaluation", Journal of Electronic Imaging 13(1), 146–165 (January 2004).
  38. T. Romen Singh, Sudipta Roy, and Kh. Manglem Singh," Local Adaptive Automatic Binarization(LAAB" ,International Journal of Computer Applications (0975 – 8887) Volume 40– No. 6, February 2012.
Index Terms

Computer Science
Information Sciences

Keywords

Binarization local thresholding local min local max and local mean