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Study of parameters in performing binarization of document images

Author Affiliations

  • 1Department of Computer Science and Engineering, Bhilai Institute of Technology, Durg, CG, India
  • 2Department of Computer Science and Engineering, Bhilai Institute of Technology, Durg, CG, India

Res. J. Computer & IT Sci., Volume 5, Issue (3), Pages 23-25, May,20 (2017)

Abstract

Now-a-days, all important paper documents flow within the transacting channels of real time working organisations are bound to perform document scans for their long term usage and gaining clarity during their frequent fetches. This paper discusses challenging issues undertaken in document image binarization. Upon resolving such binarization issues that are caused during printing, digitization and transmission process, image quality can be improved that helps in feature analysis.

References

  1. Chauhan Shivani, Sharma Ekta and Doegar Amit (2016)., Binarization Techniques for Degraded Document Images - A Review., in 5th international conference in reliability, infocom technologies and optimization(trends and future directions), 163-166.
  2. Feng Meng-Ling and Tan Yap-Peng (2004)., Contrast adaptive binarization of low quality document images, IEICE Electronics Express, 1(16), 501-506.
  3. Chamchong Rapeeporn and Fung Chun Che (2015)., A Framework for the Selection of Binarization Techniques on Palm Leaf Manuscripts Using Support Vector Machine., Advances in Decision Sciences, 2015, 1-7. doi:10.1155/2015/925935.
  4. Doermann David and Tombre Karl (2014)., Handbook of Document Image Processing and Recognition., Springer Publishing Company, Incorporated.
  5. Otsu Nobuyuki (1979)., A Threshold Selection Method from Gray-Level Histograms., IEEE, 9(1), 62-66.
  6. Singh Imocha O., Sinam Tejmani, James O. and Singh Romen T. (2012)., Local Contrast and Mean Thresholding in Image Binarization., International Jounal of Computer Applications (0975-8887), 51(6), 5-10.
  7. Sauvola Jaakko and Pietikäinen Matti (2000)., Adaptive Document Image Binarization., Pattern Recognition, 33(2), 225-236.
  8. Singh Romen T., Roy Sudipta, and Singh Kh. Manglem (2012)., Local Adaptive Automatic Binarisation (LAAB)., International Journal of Computer Applications, 40(6), 27-30.
  9. Bernsen J. (1986)., Dynamic Thresholding of Gray Level Image., in ICPR`86: Proceedings of International Conference on Pattern Recognition, 2, 1251-1255.
  10. Singh Romen T., Roy Sudipta, Sinam Tejmani, Singh O. Imocha and Singh Kh. (2011)., A New Local Adaptive Thresholding Techniques in Binarization., International Journal of Computer Science, 8(6), 271-277.
  11. Chaki Nabendu, Shaikh Soharab Hossain and Saeed K. (2014)., A Comprehensive Survey on Image Binarization Techniques., in Exploring Image Binarization Techniques. New Delhi: Springer India, ch. 2, 5-15.
  12. Gatos Basilios, Pratikakis Ioannis and Perantonis Stavros J. (2006)., Adaptive Degraded Document Image Binarization., Pattern recognition, 39(3), 317-327.
  13. Ntirogiannis K., Gatos B. and Pratikakis I. (2008)., An Objective Evaluation Methodology for Document Image Binarization Techniques., in The Eighth IAPR International Workshop on Document Analysis Systems, Nara, Japan, 217-224.
  14. Ntirogiannis K., Gatos B. and Pratikakis I. (2013)., A Performance Evaluation Methodology for Historical Document Image Binarization., IEEE Trans. on Image Proc., 22(2), 595-609.