A Contrast Independent Algorithm for Binarization of Document Images
Subject Areas : electrical and computer engineering
M.
Valizadeh
1
(Tarbiat Modares University)
E.
Kabir
2
(Tarbiat Modares University)
Abstract :
In this paper, we present a contrast independent algorithm for binarization of degraded document images. The proposed algorithm does not require any parameter setting by user. Therefore, it can handle document images with variable foreground and background intensities and low contrast documents. The proposed algorithm involves three consecutive stages. At the first stage, independent of contrast between foreground and background, sensible parts of each character are extracted using the modified water flow model, which is designed for the extraction of sensible part of each character and the drawbacks of water flow model are solved in this algorithm. In the second stage, the gray levels of foreground are estimated using the extracted text pixels and the gray levels of background are locally estimated by averaging the original image. At the third stage, for each pixel of image, the average of estimated foreground and background gray levels is defined as local threshold. After extensive experiments, the proposed binarization algorithm demonstrates superior performance against conventional binarization algorithms on a set of degraded document images captured with camera. Proposed algorithm efficiently extracts the low contrast texts.
[1] N. Otsu, "A threshold selection method from grey level histogram," IEEE Trans. Syst. Man Cybernetics., vol. 9, no. 1, pp. 62-66, Jan. 1979.
[2] J. N. Kapur, P. K. Sahoo, and A. K. C. Wong, "A new method for graylevel picture thresholding using the entropy of the histogram," Computer Vision, Graphics and Image Processing, vol. 29, no. 3, pp. 273-285, Mar. 1985.
[3] J. S. Weszka and A. Rosenfeld, "Histogram modification for threshold selection," IEEE Trans. on Systems, Man, Cybernetics, vol. 9, no. 1, pp. 38-52, Jan. 1979.
[4] B. Gatos, I. Pratikakis, and S. J. Perantonis, "Adaptive degraded document image binarization," Pattern Recognition, vol. 39, no. 3, pp. 317-327, Mar. 2006.
[5] J. Sauvola and M. Pietikainen, "Adaptive document image binarization," Pattern Recognition, vol. 33, no. 2, pp. 225-236, Feb. 2000.
[6] J. Bernsen, "Dynamic thresholding of grey-level images," in Proc. of the 8th Int. Conf. on Pattern Recognition, pp. 1251-1255, Paris, France, Oct. 1986.
[7] W. Niblack, An Introduction to Digital Image Processing, Prentice Hall, Englewood Cliffs, NJ, pp. 115-116, 1986.
[8] Y. Yang and H. Yan, "An adaptive logical method for binarization of degraded document images," Pattern Recognition, vol. 33, no. 5, pp. 787-807, May 2000.
[9] M. Kamel and A. Zhao, "Extraction of binary character/graphics images from grayscale document images," Graphical Models Image Processing, vol. 55, no. 3, pp. 203-217, May 1993.
[10] J. M. White and G. D. Rohrer, "Image segmentation for optical character recognition and other applications requiring character image extraction," IBM J. Research Development, vol. 27, no. 1, pp. 400-411, Feb. 1983.
[11] O. D. Trier and T. Taxt, "Improvement of 'Integrated Function Algorithm' for binarization of document images," Pattern Recognition Letters, vol. 16, no. 3, pp. 277-283, Mar. 1995.
[12] S. Rodtook and Y. Rangsanseri, "Adaptive thresholding of document images based on Laplacian sign," in Proc. Int. Conf. on Information Technology: Coding and Computing, pp. 501-505, 2-4 Apr. 2001.
[13] Q. Chena, Q. Suna, P. A. Heng, and D. Xia, "A double-threshold image binarization method based on edge detector," Pattern Recognition, vol. 41, no. 4, pp. 1254-1267, Apr. 2008.
[14] X. Ye, M. Cheriet, and C. Y. Suen, "Stroke-model-based character extraction from gray-level document images," IEEE Trans. on Image Processing, vol. 10, no. 8, pp. 1152-1161, Aug. 2001.
[15] S. Huang, M. Ahmadi, and M. A. Sid-Ahmed, "A hidden Markov model-based character extraction method," Pattern Recognition, vol. 41, no. 9, pp. 2890-2900, Sep. 2008.
[16] O. D. Trier and T. Taxt, "Evaluation of binarization methods for document images," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 17, no. 3, pp. 312-315, Mar. 1995.
[17] M. Sezgin and B. Sankur, "Survey over image thresholding techniques and quantitative performance evaluation," J. of Electronic Imaging, vol. 13, no. 1, pp. 146-165, Jan. 2004.
[18] Y. Chen and G. Leedham, "Decompose algorithm for thresholding degraded historical document images," in IEE Proc. Vis. Image Signal Processing, vol. 152, pp. 702-714, Dec. 2005.
[19] J. R. Parker, "Gray level thresholding in badly illuminated images," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 13, no. 8, pp. 813-819, Aug. 1991.
[20] I. K. Kim, D. W. Jung, and R. H. Park, "Document image binarization based on topographic analysis using a water flow model," Pattern Recognition, vol. 35, no. 1, pp. 265-277, Jan. 2002.
[21] F. Shafait, D. Keysers, and T. M. Breuel, "Efficient implementation of local adaptive thresholding techniques using integral images," in Document Recognition and Retrieval XV, 2008.
[22] E. Badekas and N. Papamarkos, "Optimal combination of document binarization techniques using a self - organizing map neural network," Engineering Applications of Artificial Intelligence, vol. 20, no. 1, pp. 11-24, Feb. 2007.
[23] Y. Solihin and C. G. Leedham, "Integral ratio: a new class of global thresholding techniques for handwriting images," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 761-768, Aug. 1999.