Skip to main content
SpringerLink
Log in

Handwritten Arabic Manuscript Image Binarization Using Sine Cosine Optimization Algorithm

  • Conference paper
  • First Online:
Genetic and Evolutionary Computing (ICGEC 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 536))

Included in the following conference series:

Abstract

Historic manuscript image binarization is considered an important step due to the different kinds of degradation effects on optical character recognition (OCR) or word spotting systems. Previous methods failed on to find the optimal threshold for binarization. In this paper, we investigate the effects of sine cosine algorithm (SCA) on reducing the compactness K-means Clustering as the objective function. The SCA searches for the optimal clustering of the given handwritten manuscript image into compact clusters under some constraints. The proposed approach is evaluated and assessed on a set of selected handwritten Arabic manuscript images. The Experimental result shows that the proposed approach provides the highest value than the famous binarization methods such as; Otsu’s and Niblack’s in terms of F-measure, Pseudo- F-measure, PSNR, Geometric accuracy and the low value on DRD, NRM, MPM.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Su, B., Lu, S., Tan, C.L.: Robust document image binarization technique for degraded document images. IEEE Trans. Image Process. 22(4), 1408–1417 (2013)

    Article  MathSciNet  Google Scholar 

  2. Guo, Y., Cheng, H.-D.: New neutrosophic approach to image segmentation. Pattern Recogn. 42(5), 587–595 (2009)

    Article  MATH  Google Scholar 

  3. Smarandache, F.: A Unifying Field in Logics Neutrosophic Logic. Neutrosophy, Neutrosophic Set, Neutrosophic Probability, 3rd edn. American Research Press, Santa Fe (2003)

    MATH  Google Scholar 

  4. Sauvola, J., Pietikinen, M.: Adaptive document image binarization. Pattern Recogn. 33(2), 225–236 (2000)

    Article  Google Scholar 

  5. Amin, K.M., Abd Elfattah, M., Hassanien, A.E., Schaefer, G.: A binarization algorithm for historical arabic manuscript images using a neutrosophic approach. In: The 9th International Conference on Computer Engineering and Systems (ICCES), Egypt, pp. 266–270. IEEE (2014)

    Google Scholar 

  6. Hassanien, A.E., Abd Elfattah, M., Amin, K.M., Mohamed, S.: A novel hybrid binarization technique for images of historical Arabic manuscripts. Stud. Inform. Control 24(3), 271–282 (2015). ISSN 1220–1766

    Google Scholar 

  7. Abd Elfattah, M., Hassanien, A.E., Mostafa, A., Ali, A.F., Amin, K.M., Mohamed, S.: Artificial bee colony optimizer for historical Arabic manuscript images binarization. In: The 11th International Conference on Computer Engineering (ICENCO), Egypt, pp. 251–255. IEEE (2015)

    Google Scholar 

  8. Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report University, Engineering Faculty, Computer Eng Department (2005)

    Google Scholar 

  9. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  10. Otsu, N.: A thresholding selection method from gray-level histogram. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  MathSciNet  Google Scholar 

  11. Niblack, W.: An introduction to digital image processing, pp. 115–116. Prentice-Hall, Englewood Cliffs (1986)

    Google Scholar 

  12. Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl. Based Syst. 96, 120–133 (2016). Elsevier

    Article  Google Scholar 

  13. MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1(14), pp. 281–297 (1967)

    Google Scholar 

  14. http://wqf.me/. Accesssed 12 Apr 2016, 8.00 P.M

  15. Paredes, R., Kavallieratou, E., Lins, R.D.: ICFHR 2010 contest: quantitative evaluation of binarization algorithms. In: 2010 International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 733–736. IEEE (2010)

    Google Scholar 

  16. Ntirogiannis, K., Gatos, B., Pratikakis, I.: An objective evaluation methodology for document image binarization techniques. In: The 8th IAPR International Workshop on Document Analysis Systems (DAS 2008), Nara Prefectural New Public Hall, Nara, Japan, 17–19 September 2008, pp. 217–224 (2008)

    Google Scholar 

  17. Lu, H., Kot, A.C., Shi, Y.Q.: Distance-reciprocal distortion measure for binary document images. IEEE Sig. Process. Lett. 11(2), 228–231 (2004)

    Article  Google Scholar 

  18. Ntirogiannis, K., Gatos, B., Pratikakis, I.: Performance evaluation methodology for historical document image binarization. IEEE Trans. Image Process. 22(2), 595–609 (2013)

    Article  MathSciNet  Google Scholar 

  19. Ntirogiannis, K., Gatos, B., Pratikakis, I.: ICFHR2014 competition on handwritten document image binarization (H-DIBCO 2014). In: 2014 14th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 809–813. IEEE (2014)

    Google Scholar 

  20. Pratikakis, I., Gatos, B., Ntirogiannis, K.: H-DIBCO 2010-handwritten document image binarization competition. In: 2010 International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 727–732. IEEE (2010)

    Google Scholar 

  21. Mostafa, A., Fouad, A., Abd Elfattah, M., Hassanien, A.E., Hefny, H., Zhu, S.Y., Schaefer, G.: CT liver segmentation using artificial bee colony optimisation. Procedia Comput. Sci. 60, 1622–1630 (2015)

    Article  Google Scholar 

  22. Abd Elfattah, M., Waly, M.I., Elsoud, M.A.A., Hassanien, A.E., Tolba, M.F., Platos, J., Schaefer, G.: An improved prediction approach for progression of ocular hypertension to primary open angle glaucoma. In: Kömer, P., Abraham, A., Snášel, V. (eds.) Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014. AISC, vol. 303, pp. 405–412. Springer, Heidelberg (2014). doi:10.1007/978-3-319-08156-4_40

    Google Scholar 

  23. Gaber, T., Ismail, G., Anter, A., Soliman, M., Ali, M., Semary, N., Hassanien, A.E., Snasel, V.: Thermogram breast cancer prediction approach based on Neutrosophic sets and fuzzy c-means algorithm. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4254–4257. IEEE, August 2015

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computers and Information, Mansoura University, Mansoura, Egypt

    Mohamed Abd Elfattah & Sherihan Abuelenin

  2. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  3. Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China

    Jeng-Shyang Pan

  4. Scientific Research Group in Egypt (SRGE), Cairo, Egypt

    Mohamed Abd Elfattah & Aboul Ella Hassanien

Authors
  1. Mohamed Abd Elfattah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sherihan Abuelenin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aboul Ella Hassanien
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jeng-Shyang Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohamed Abd Elfattah .

Editor information

Editors and Affiliations

  1. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

  2. School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, China

    Jerry Chun-Wei Lin

  3. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Chia-Hung Wang

  4. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Xin Hua Jiang

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Abd Elfattah, M., Abuelenin, S., Hassanien, A.E., Pan, JS. (2017). Handwritten Arabic Manuscript Image Binarization Using Sine Cosine Optimization Algorithm. In: Pan, JS., Lin, JW., Wang, CH., Jiang, X. (eds) Genetic and Evolutionary Computing. ICGEC 2016. Advances in Intelligent Systems and Computing, vol 536. Springer, Cham. https://doi.org/10.1007/978-3-319-48490-7_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-48490-7_32

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48489-1

  • Online ISBN: 978-3-319-48490-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation