Open Access Journal

ISSN : 2394-6849 (Online)

International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

Open Access Journal

International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

ISSN : 2394-6849 (Online)

De-Noising Of Degraded Document Image Using Adaptive and OTSU Thresholding Techniques

Author : Chittari Padma 1 Pullarevu Sreenivasulu 2

Date of Publication :7th August 2016

Abstract: Segmentation of text from badly degraded document an image is a very challenging task due to the high inters/intra variation between the document background and the foreground text of different document images. In this project, we propose a novel document image binarization technique that addresses these issues by using adaptive image contrast. The adaptive image contrast is a combination of the local image contrast and the local image gradient that is tolerant to text and background variation caused by different types of document degradations. In the proposed technique, an adaptive contrast map is first constructed for an input degraded document image. The contrast map is then binarized and combined with Cannys edge map to identify the text stroke edge pixels. The document text is further segmented by a local threshold that is estimated based on the intensities of detected text stroke edge pixels within a local window. The proposed method is simple, robust, and involves minimum parameter tuning

Reference :

  1. [1] Bolan, Shijanlu, and Chew Lim Tan.”Robust document image binarization technique for degraded document images.”Image processing, IEEE Transactions on 22.4(2013):1408-1417

    [2] B. Gatos, K. Ntirogiannis, and I. Pratikakis, “ICDAR 2009 document image binarization contest (DIBCO 2009),” in Proc. Int. Conf. Document Anal. Recognit.,Jul.2009, pp.1375–1382.

    [3] I.Pratikakis,B. Gatos, and K Ntirogiannis,“ICDAR 2011 document image binarization contest (DIBCO 2011),” in Proc. Int. Conf. Document Anal. Recognit., Sep. 2011, pp. 1506–1510

    [4] I.Pratikakis ,B.Gatos, and K. Ntirogiannis, “H-DIBC2010 hand- writtendocument image binarization competition,” in Proc. Int. Conf. Frontiers Handwrit. Recognit., Nov. 2010, pp.727–732.

    [5] B.Su,S. Lu, and C.L.Tan,“Binarizationof historical handwritten images using local maximum and minimum filter,”in Proc.Int.Workshop Anal.Syst.,Jun.2010,pp.159–166.

    [6] Leedham, C. Yan, K. Takru, J. Hadi, N. Tan, and L. Mian, “Compar-ison of some thresholding algorithms for text/background segmentation in difficult document images,” in Proc. Int. Conf. Document Anal. Recognit., vol. 13. 2003, pp. 859–864.

    [7] M. Sezgin and B. Sankur,“Survey over image thresholding techniques and quantitative performance evaluation,” vol. 13, no. 1, pp. 146–165, Jan. 2004.

    [8] O.D.Trier and A. K. Jain,“Goal-directed evaluation of binarization methods,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 12, pp. 1191– 1201, Dec. 1995.

    [9] O.D. Trier and T. Taxt, “Evaluation of binarization methods for document images,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 3, pp. 312–315, Mar. 1995.

    [10] A.Brink,“ Thresholding of digital images using twodimensional entropies,”Pattern Recognit.,vol.25, no. 8, pp.803–808,1992.

    [11] J. Kittler and J. Illingworth, “On threshold selection using clustering criteria,” IEEE Trans. Syst., Man, Cybern., vol. 15, no. 5, pp. 652–655, Sep–Oct.1985.

    [12] N. Otsu, “A threshold selection method from gray level histogram,” IEEE Trans.Syst.,Man,Cybern.,vol.19, no. 1, pp.62–66, Jan.1979.


Recent Article