Abstract
Binarization of historical documents is difficult and is still an open area of research. In this paper, a new binarization technique for document images is presented. The proposed technique is based on the most commonly used binarization method: Sauvola’s, which performs relatively well on classical documents, however, three main defects remain: the window parameter of Sauvola’s formula does not fit automatically to the image content, is not robust to low contrasts, and not invariant with respect to contrast inversion. Thus on documents such as magazines, the content may not be retrieved correctly. In this paper we use the image contrast that is defined by the local image minimum and maximum in combination with the computed Sauvola’s binarization step to guarantee good quality binarization for both low and correctly contrasted objects inside a single document, without adjusting manually the user-defined parameters to the document content.
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- 1.
Numerically, this is done by using the function bwareaopen of Matlab.
References
Otsu, N.: A thresholding selection method from gray-level histogram. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Kapur, J.N., Sahoo, P.K., Wong, A.K.C.: A new method for gray-level picture thresholding using the entropy of the histogram. Graph. Image Process. 29, 273–285 (1985)
Kittler, J., Illingworth, J.: Minimum error thresholding. Pattern Recognit. 19(1), 41–47 (1986)
Niblack, W.: An Introduction to Digital Image Processing. Prentice Hall, Englewood Cliffs (1986)
Sauvola, J., Pietikainen, M.: Adaptive document image binarization. Pattern Recognit. 33(2), 225–236 (2000)
Bernsen, J.: Dynamic thresholding of grey-level images. In: Proceedings of the Eighth International Conference on Pattern Recognition, Paris, France, pp. 1251–1255, October 1986
Wolf, C., Jolion, J.M.: Extraction and recognition of artificial text in multimedia documents. Pattern Anal. Appl. 6(4), 309–326 (2003)
Feng, M.L., Tan, Y.P.: Contrast adaptive binarization of low quality document images. IEICE Electron. Express 1(16), 501–506 (2004)
Kim, I.K., Jung, D.W., Park, R.H.: Document image binarization based on topographic analysis using a water flow model. Pattern Recogn. 35(1), 265–277 (2002)
Gatos, B., Pratikakis, I., Perantonis, S.J.: Adaptive degraded document image binarization. Pattern Recogn. 39(3), 317–327 (2006)
Lu, S., Su, B., Tan, C.L.: Document image binarization using background estimation and stroke edges. Int. J. Doc. Anal. Recogn. 13(4), 303–314 (2010)
Ntirogiannis, K., Gatos, B., Pratikakis, I.: A combined approach for the binarization of handwritten document images. Pattern Recogn. Lett. - Spec. Issue Front. Handwrit. Process. 35, 3–15 (2012). doi:10.1016/j.patrec.2012.09.026
Moghaddam, R.F., Cheriet, M.: RSLDI: restoration of singlesided low-quality document images. Pattern Recogn. 42(12), 3355–3364 (2009)
Howe, N.: Document binarization with automatic parameter tuning. Int. J. Doc. Anal. Recogn. 16, 247–258 (2012)
Su, B., Lu, S., Tan, C.L.: Binarization of historical handwritten document images using local maximum and minimum filter. In: International Workshop on Document Analysis Systems, pp. 159–165, June 2010
Hadjadj, Z., Meziane, A., Cheriet, M., Cherfa, Y.: An active contour based method for image binarization: application to degraded historical document images. In: ICFHR 2014, Crete, Greece, pp. 655–660 (2014). doi:10.1109/ICFHR.2014.115
Moghaddam, R.F., Cheriet, M.: A multi-scale framework for adaptive binarization of degraded document images. Pattern Recogn. 43(6), 2186–2198 (2010)
Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13, 146–165 (2004)
Badekas, E., Papamarkos, N.: Automatic evaluation of document binarization results. In: Sanfeliu, A., Cortés, M.L. (eds.) CIARP 2005. LNCS, vol. 3773, pp. 1005–1014. Springer, Heidelberg (2005)
Rangoni, Y., Shafait, F., Breuel, T.M.: OCR based thresholding. In: Proceedings of IAPR Conference on Machine Vision Applications, pp. 98–101 (2009)
Cheriet, M., Moghaddam, R.F., Hedjam, R.: A learning framework for the optimization and automation of document binarization methods. Comput. Vis. Image Underst. (CVIU) 117(3), 269–280 (2013)
Lazzara, G., Géraud, T.: Efficient multiscale Sauvola’s binarization. Int. J. Doc. Anal. Recogn. 17(2), 105–123 (2014)
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Hadjadj, Z., Meziane, A., Cherfa, Y., Cheriet, M., Setitra, I. (2016). ISauvola: Improved Sauvola’s Algorithm for Document Image Binarization. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_82
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DOI: https://doi.org/10.1007/978-3-319-41501-7_82
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