Abstract
In this paper, an algorithm is proposed for card images binarization. It is performed by three steps: coarse binarization, refined binarization, and postprocessing. Firstly, it uses the traditional global thresholding approach to separate a card image into several sub-images, which can be classified into two classes: text sub-images with clear background and text sub-images with complicated background. Secondly, the dual-thresholding is applied to regenerate or retouch the sub-images. According to the characteristics of text candidate sub-image, the thresholding method is selected and applied on it. Finally, the postprocessing is performed on the binary image. Experimental results demonstrate that this approach highly improved the performance of the card image binarization system.
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References
Otsu, N.: A Threshold Selection Method from Grey Level Histogram. IEEE Transactions on Systems, Man and Cybernetics 9(1), 62–66 (1979)
Kittler, J., Illingworth, J.: Minimum Error Thresholding. Pattern Recognition 19, 41–47 (1986)
Huang, L.K., Wang, M.J.: Image Thresholding by Minimizing the Measure of Fuzziness. Pattern Recognition 28, 41–51 (1995)
Bernsen, J.: Dynamic Thresholding of Grey-level Images. In: 8th International Conference on Pattern Recognition, France-Paris, pp. 251–255. IEEE Computer Society, Los Alamitos (1986)
Niblack, W.: An Introduction to Image Processing, pp. 115–116. Prentice-Hall, Englewood Cliffs (1986)
Sauvola, J., Pietikainen, M.: Adaptive Document Image Binarization. Pattern Recognition 33, 225–236 (2000)
Wu, S., Amin, A.: Automatic Thresholding of Gray-level Using Multi-stage Approach. In: 7th International Conference on Document Analysis and Recognition, vol. 1, pp. 493–497. IEEE Computer Society, Washington (2003)
Solihin, Y., Leedham, C.: Integral Ratio: A New Class of Thresholding Techniques for Handwriting Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 761–768 (1999)
Pal, N.R., Pal, S.K.: Entropic Thresholding. Signal Process 16, 97–108 (1989)
Du, Y., Chang, C., Thouin, P.D.: An Unsupervised Approach to Color Video Thresholding. Optical Engineering 43(2), 282–289 (2004)
Sezgin, M., Sankur, B.: Survey over Image Thresholding Techniques and Quantitative Performance Evaluation. Journal of Electronic Imaging 13(1), 146–165 (2004)
Wang, S.Z., Lee, H.J.: Dual-Binarization and Anisotropic Diffusion of Chinese Characters in Calligraphy Documents. In: 6th International Conference on Document Analysis and Recognition, pp. 271–275. IEEE Computer Society, Washington (2001)
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Liu, C., Miao, D., Wang, C. (2008). Card Images Binarization Based on Dual-Thresholding Identification. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science(), vol 5227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85984-0_139
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DOI: https://doi.org/10.1007/978-3-540-85984-0_139
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85983-3
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