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Adaptive Binarization Method for Enhancing Ancient Malay Manuscript Images

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7106))

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Abstract

In order to transform ancient Malay manuscript images to be cleaner and more readable, enhancement must be performed as the images have different qualities due to uneven background, ink bleed, or ink bleed and expansion of spots. The proposed method for image improvement in this experiment consists of several stages, which are Local Adaptive Equalization, Image Intensity Values, K-Means Clustering, Adaptive Thresholding, and Median Filtering. The proposed method produces an adaptive binarization image. We tested the proposed method on eleven ancient Malay manuscript images. The proposed method has the smallest average value of Relative Foreground Area Error compared to the other state of the art methods. At the same time, the proposed method have produced the better results and better readability compared to the other methods.

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References

  1. Gatos, B., Pratikakis, I., Perantonis, S.J.: Improved Document Image Binarization by Using a Combination of Multiple Binarization Techniques and Adapted Edge Information. In: 19th International Conference on Pattern Recognition (ICPR), Tampa, Florida, USA, pp. 1–4 (2008) ISBN: 978-1-4244-2175-6/08

    Google Scholar 

  2. Yosef, I.B., Beckman, I., Kedem, K., Dinstein, I.: Binarization, Character Extraction, and Writer Identification of Historical Hebrew Calligraphy Documents. IJDAR 9, 89–99 (2007)

    Article  Google Scholar 

  3. Shafait, F., Keysers, D., Breuel, T.M.: Efficient Implementation of Local Adaptive Thresholding Techniques Using Integral Images. In: Proc. SPIE. Document Recognition and Retrieval XV (2008)

    Google Scholar 

  4. Milewski, R., Govindaraju, V.: Binarization and Cleanup of Handwritten Text from Carbon Copy Medical Form Images. Pattern Recognition 41, 1308–1315 (2008)

    Article  Google Scholar 

  5. Arora, S., Acharya, J., Verma, A., Panigrahi, P.K.: Multilevel Thresholding for Image Segmentation through a Fast Statistical Recursive Algorithm. Pattern Recognition Letters 29, 119–125 (2008)

    Article  Google Scholar 

  6. Bataineh, B., Abdullah, S.N.H.S., Omar, K.: An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows. Journal of Pattern Recognition Letters 32, 1805–1813 (2011)

    Article  Google Scholar 

  7. Niblack, W.: An Introduction to Digital Image Processing. Prentice Hall, Upper Saddle River (1985)

    Google Scholar 

  8. Khurshid, K., Siddiqi, I., Faure, C., Vincent, N.: Comparison of Niblack Inspired Binarization Methods for Ancient Documents. In: 16th International Conference on Document Recognition and Retrieval. SPIE, USA (2010)

    Google Scholar 

  9. Kefali, A., Sari, T., Sellami, M.: Evaluation of Several Binarization Techniques for Old Arabic Documents Images. In: The First International Symposium on Modeling and Implementing Complex Systems, MISC 2010, Constantine, Algeria, pp. 88–99 (2010)

    Google Scholar 

  10. Bataineh, B., Abdullah, S.N.H.S., Omar, K., Faidzul, M.: Adaptive Thresholding Methods for Documents Image Binarization. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Ben-Youssef Brants, C., Hancock, E.R. (eds.) MCPR 2011. LNCS, vol. 6718, pp. 230–239. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  11. Boussellaa, W., Bougacha, A., Zahour, A., El Abed, H., Alimi, A.: Enhanced Text Extraction from Arabic Degraded Document Images using EM Algorithm. In: 10th International Conference on Document Analysis and Recognition, pp. 743–747 (2009)

    Google Scholar 

  12. António, A., Leite, R., Cancela, M.L., Shahbazkia, H.R.: MAQ – A Bioinformatics Tool for Automatic Macroarray Analysis. International Journal of Computer Applications 4, 51–58 (2010)

    Article  Google Scholar 

  13. Atae-Allah, Z., Aroza, J.M.: A Filter to Remove Gaussian Noise by Clustering the Gray Scale. Journal of Mathematical Imaging and Vision 17(1), 15–25 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  14. Manuscripts, National Library of Malaysia (Perpustakaan Negara Malaysia, PNM) (April 27, 2009), http://www.pnm.gov.my/pnmv3/index.php?id=84

  15. Sezgin, M., Sankur, B.: Survey Over Image Thresholding Techniques and Quantitative Performance Evaluation. J. Electron Imaging 13(1), 146–165 (2004)

    Article  Google Scholar 

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Yahya, S.R., Sheikh Abdullah, S.N.H., Omar, K., Liong, CY. (2011). Adaptive Binarization Method for Enhancing Ancient Malay Manuscript Images. In: Wang, D., Reynolds, M. (eds) AI 2011: Advances in Artificial Intelligence. AI 2011. Lecture Notes in Computer Science(), vol 7106. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25832-9_63

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  • DOI: https://doi.org/10.1007/978-3-642-25832-9_63

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25831-2

  • Online ISBN: 978-3-642-25832-9

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