Skip to main content
SpringerLink
Log in

Multi-verse Optimization Clustering Algorithm for Binarization of Handwritten Documents

  • Conference paper
  • First Online:
Recent Trends in Signal and Image Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 727))

Abstract

Binarization process of images of historical manuscripts is considered a challenge due to the different types of noise that are related to the degraded manuscripts. This paper presents an automatic clustering algorithm for binarization of handwritten documents (HD) based on multi-verse optimization. The multi-verse algorithm is used to find cluster centers in HD where the number of clusters is predefined. The proposed approach is tested on the benchmarking dataset used in the Handwritten Document Image Binarization Contest (H-DIBCO 2014). The proposed approach is assessed through several performance measures. The experimental results achieved competitive outcomes compared to the well-known binarization methods such as Otsu and Sauvola.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Mesquita RG, Silva RM, Mello CA, Miranda PB (2015) Parameter tuning for document image binarization using a racing algorithm. Expert Syst Appl 42(5):2593–2603

    Google Scholar 

  2. Lu H, Kot AC, Shi YQ (2004) Distance-reciprocal distortion measure for binary document images. IEEE Signal Process Lett 11(2):228–231

    Google Scholar 

  3. Singh BM, Sharma R, Ghosh D, Mittal A (2014) Adaptive binarization of severely degraded and non-uniformly illuminated documents. Int J Doc Anal Recognit (IJDAR) 17(4):393–412

    Google Scholar 

  4. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66

    Google Scholar 

  5. Kapur JN, Sahoo PK, Wong AK (1985) A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vis Graph Image Process 29(3):273–285

    Google Scholar 

  6. Kittler J, Illingworth J (1986) Minimum error thresholding. Pattern Recognit 19(1):41–47

    Google Scholar 

  7. Niblack W (1985) An introduction to digital image processing. Strandberg Publishing Company

    Google Scholar 

  8. Sauvola J, Pietikäinen M (2000) Adaptive document image binarization. Pattern Recognit 33(2):225–236

    Google Scholar 

  9. Bernsen J (1986) Dynamic thresholding of grey-level images. Int Conf Pattern Recognit 2:1251–1255

    Google Scholar 

  10. Hadjadj Z, Cheriet M, Meziane A, Cherfa Y (2017) A new efficient binarization method: application to degraded historical document images. Signal Image Video Process 1–8

    Google Scholar 

  11. Mirjalili S, Mirjalili S, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2)

    Google Scholar 

  12. MacQueen J et al (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Oakland, CA, USA, vol 1, pp 281–297

    Google Scholar 

  13. Ntirogiannis K, Gatos B, Pratikakis I (2014) ICFHR2014 competition on handwritten document image binarization (h-dibco 2014). In: 2014 14th international conference on frontiers in handwriting recognition (ICFHR). IEEE, pp 809–813

    Google Scholar 

  14. http://transcriptorium.eu

  15. Gatos B, Ntirogiannis K, Pratikakis I (2009) ICDAR 2009 document image binarization contest (DIBCO 2009). In: 10th international conference on document analysis and recognition, 2009 (ICDAR’09). IEEE, pp 1375–1382

    Google Scholar 

  16. Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manag 45(4):427–437

    Google Scholar 

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

    Google Scholar 

  18. Pratikakis I, Gatos B, Ntirogiannis K (2010) H-dibco 2010-handwritten document image binarization competition. In: 2010 international conference on frontiers in handwriting recognition (ICFHR). IEEE, pp 727–732

    Google Scholar 

  19. Young DP, Ferryman JM (2005) Pets metrics: on-line performance evaluation service. In: Joint IEEE international workshop on visual surveillance and performance evaluation of tracking and surveillance (VS-PETS), pp 317–324

    Google Scholar 

  20. Elfattah MA, Abuelenin S, Hassanien AE, Pan JS (2016) Handwritten arabic manuscript image binarization using sine cosine optimization algorithm. In: International conference on genetic and evolutionary computing. Springer, pp 273–280

    Google Scholar 

  21. Mostafa A, Fouad A, Elfattah MA, Hassanien AE, Hefny H, Zhu SY, Schaefer G (2015) Ct liver segmentation using artificial bee colony optimisation. Procedia Comput Sci 60:1622–1630

    Google Scholar 

  22. Mostafa A, Elfattah MA, Fouad A, Hassanien AE, Hefny H (2016) Wolf local thresholding approach for liver image segmentation in ct images. In: Proceedings of the second international Afro-European conference for industrial advancement (AECIA 2015). Springer, pp 641–651

    Google Scholar 

  23. Ali AF, Mostafa A, Sayed GI, Elfattah MA, Hassanien AE (2016) Nature inspired optimization algorithms for ct liver segmentation. In: Medical imaging in clinical applications. Springer, pp 431–460

    Google Scholar 

  24. Hassanien AE, Elfattah MA, Aboulenin S, Schaefer G, Zhu SY, Korovin I (2016) Historic handwritten manuscript binarisation using whale optimisation. In: 2016 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 003842–003846

    Google Scholar 

  25. Sahlol AT, Suen CY, Zawbaa HM, Hassanien AE, Elfattah MA (2016) Bio-inspired bat optimization algorithm for handwritten arabic characters recognition. In: 2016 IEEE congress on evolutionary computation (CEC). IEEE, pp 1749–1756

    Google Scholar 

  26. Sahlol A, Elfattah MA, Suen CY, Hassanien AE (2016) Particle swarm optimization with random forests for handwritten arabic recognition system. In: International conference on advanced intelligent systems and informatics. Springer, pp 437–446

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computers and Information, Computer Science Department, Mansoura University, Dakahlia Governorate, Egypt

    Mohamed Abd Elfattah & Sherihan Abuelenin

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

    Aboul Ella Hassanien

  3. Department of Computer Application, RCC Institute of Information Technology, Kolkata, 700015, India

    Siddhartha Bhattacharyya

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

    Mohamed Abd Elfattah, Aboul Ella Hassanien & Siddhartha Bhattacharyya

Authors
  1. Mohamed Abd Elfattah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aboul Ella Hassanien
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sherihan Abuelenin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Siddhartha Bhattacharyya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Siddhartha Bhattacharyya .

Editor information

Editors and Affiliations

  1. Department of Computer Application, RCC Institute of Information Technology, Kolkata, West Bengal, India

    Siddhartha Bhattacharyya

  2. Department of Engineering Science and Management, RCC Institute of Information Technology, Kolkata, West Bengal, India

    Anirban Mukherjee

  3. Department of Information Technology, RCC Institute of Information Technology, Kolkata, West Bengal, India

    Hrishikesh Bhaumik

  4. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

  5. Department of Human Intelligence Systems, Kyushu Institute of Technology, Wakamatsu-ku, Kitakyushu, Fukuoka, Japan

    Kaori Yoshida

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Elfattah, M.A., Hassanien, A.E., Abuelenin, S., Bhattacharyya, S. (2019). Multi-verse Optimization Clustering Algorithm for Binarization of Handwritten Documents. In: Bhattacharyya, S., Mukherjee, A., Bhaumik, H., Das, S., Yoshida, K. (eds) Recent Trends in Signal and Image Processing. Advances in Intelligent Systems and Computing, vol 727. Springer, Singapore. https://doi.org/10.1007/978-981-10-8863-6_17

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-8863-6_17

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8862-9

  • Online ISBN: 978-981-10-8863-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation