Abstract
Ancient manuscripts have been preserved by many organizations so as to protect these documents and retrieve traditional knowledge. With the advanced computer technology, digitized media is now commonly used to record these documents. One objective of such work is to develop an efficient image processing system that could be used to retrieve knowledge and information automatically from these ancient manuscripts. Binarization is a preprocessing technique used to extract text and characters from the manuscripts. The output is then used for further processes such as character recognition and knowledge extraction. This paper compares different binarization techniques that could be used for processing of ancient manuscripts. The aim is to improve the binarization techniques with the main objective of developing an automated preprocessing technique for ancient manuscript recognition and knowledge extraction.
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References
Information Heritage, http://www.emc.com/leadership/digital-universe/information-heritage-awards-2008.htm
Musiket, Y.: Scanning the Past (2009)
Mahasarakham University: Palm Leaf Manuscript Preservation Project in Northeastern Region. Reports for the Financial Year 2004 and 2005 (2005) (in Thai)
Thailand Herbal Repository Access Initiative (THRAI), http://thrai.sci.ku.ac.th/
Chamchong, R., Fung, C.C.: Comparing background elimination approaches for processing of ancient Thai manuscripts on palm leaves. In: 2009 Int. Conf. Machine Learning and Cybernetics, China (2009)
Chen, Y., Leedham, G.: Decompose algorithm for thresholding degraded historical document images. In: IEE Proceeding Visual Image Signal Processing p. 152 (2005)
He, J., Do, Q.D.M., Downton, A.C., Kim, J.H.: A comparison of binarization methods for historical archive documents. In: Proc. 8th Int. Conf. Document Analysis and Recognition, vol. 538, pp. 538–542 (2005)
Leedham, G., Chen, Y., Takru, K., Joie Hadi Nata, T., Li, M.: Comparison of some thresholding algorithms for text/background segmentation in difficult document images. In: Proc. 7th Int. Conf. Document Analysis and Recognition, pp. 859–864 (2003)
Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. J. of Electronic Imaging 13, 146–168 (2004)
Niblack, W.: An introduction to digital image processing. Prentice-Hall, Englewood Cliffs (1986)
Otsu, N.: A threshold selection method from gray-level histogram. IEEE Trans. Systems Man Cybernet 9, 62–66 (1979)
Sauvola, J., Pietikainen, M.: Adaptive document image binarization. Pattern Recognition 33, 225–236 (2000)
Sezgin, M., Sankur, B.: Selection of thresholding methods for nondestructive testing applications. In: Proc. 2001 Int. Conf. Image Processing, vol. 3, pp. 763, 764–767 (2001)
Trier, O.D., Jain, A.K.: Goal-directed evaluation of binarization methods. IEEE Trans. Pattern Analysis and Machine Intelligence 17, 1191–1201 (1995)
Badekas, E., Papamarkos, N.: Document binarisation using Kohonen SOM. IET Image Process. 1, 67–84 (2007)
Suavola, J., Kauniskangas, H.: MediaTeam Document Database II, a CD-ROM collection of document images. University of Oulu, Finland (1999)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice-Hall, New Jersey (2002)
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. Models Image Process. 29, 273–285 (1985)
Kittler, J., Illingworth, J.: Minimum error thresholding. Pattern Recognition 19, 41–47 (1986)
Tsai, W.H.: Moment-preserving thresholding: A new approch. Graph. Models Image Process. 19, 377–379 (1985)
Huang, L.-K., Wang, M.-J.J.: Image thresholding by minimizing the measures of fuzziness. Pattern Recognition 28, 41–51 (1995)
Jui-Cheng, Y., Fu-Juay, C., Shyang, C.: A new criterion for automatic multilevel thresholding. IEEE Transactions on Image Processing 4, 370–378 (1995)
Heijden, F.v.d., Duin, R.P.W., Ridder, D.d., Tax, D.M.J.: Classification, parameter estimation and state estimation: an engineering approach using MATLAB. John Wiley & Sons, Ltd., West Sussex (2004)
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Chamchong, R., Fung, C.C., Wong, K.W. (2010). Comparing Binarisation Techniques for the Processing of Ancient Manuscripts. In: Nakatsu, R., Tosa, N., Naghdy, F., Wong, K.W., Codognet, P. (eds) Cultural Computing. ECS 2010. IFIP Advances in Information and Communication Technology, vol 333. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15214-6_6
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DOI: https://doi.org/10.1007/978-3-642-15214-6_6
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