Skip to main content
SpringerLink
Log in

Best Bound Population-Based Local Search for Memetic Algorithm in View of Character Recognition

  • Conference paper
  • First Online:
Book cover Third International Congress on Information and Communication Technology

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 797))

Abstract

Memetic algorithms (MAs) are originally optimization algorithms with separate individual improvement, and they tend to fully exploit the problem area under consideration. But just like human brain, the recognition time tends to increase with increasing size of population. This paper aims to provide a logical solution using cultural evolution and local learning feature of MA. By introducing best bound population (BBP) from available set of population size, it is possible to keep recognition time in acceptable limits. The best bound population can be continuously upgraded using local search. The paper also revisits some popular techniques of character recognition using traditional approach and using genetic approach. Finally, all techniques are compared for error percentage and recognition time. The relative comparison with figures is presented to justify the findings.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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. Krasnogor N, Smith J (2005) A tutorial for competent memetic algorithms model taxonomy and design issues. IEEE Trans Evol Comput 9(5):475–488

    Article  Google Scholar 

  2. Moscato P (1989) On evolution, search, optimization, GAs and martial arts: toward memetic algorithms. California Institute of Technology Pasadena, CA, Technical Report Caltech Concurrent Computation Program, Report 826

    Google Scholar 

  3. He Mort (2000) Hybrid genetic algorithms for telecommunications network back-up routing. BT Technol J 18(4):42–56

    Article  Google Scholar 

  4. Vazquez M, Whitley L (2000) A hybrid genetic algorithm for the quadratic assignment problem. In: Proceedings of the 2nd annual conference on genetic and evolutionary computation, pp 135–142

    Google Scholar 

  5. Fleurent C, Ferland J (1993) Genetic hybrids for the quadratic assignment problem. In: DIMACS, Series in Discrete Mathematics and Theoretical Computer Science. American Mathematical Society, Providence, RI

    Google Scholar 

  6. Merz P (2000) Memetic algorithms for combinatorial optimization problems: fitness landscapes and effective search strategies. Ph.D. Dissertation, Parallel Systems Research Group, Department of Electrical Engineering Computer Science, University of Siegen, Siegen, Germany

    Google Scholar 

  7. Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ (1998) Automated docking using a lamarkian genetic algorithm and an empirical binding free energy function. J Comput Chem 14:1639–1662

    Article  Google Scholar 

  8. Ku K, Mak M (1998) Empirical analysis of the factors that affect the Baldwin Effect. In: Proceeding Parallel Problem Solving From Nature—PPSN-V (Lecture Notes in Computer Science), pp 481–490

    Google Scholar 

  9. Welekar R, Thakur NV (2015) Memetic algorithm used in character recognition. In: 5th International conference, SEMCCO 2014, Bhuvaneshwar, LNCS 8947, Springer, pp 636–646

    Google Scholar 

  10. Sevaux M, Kenneth S (2005) Permutation distance measures for memetic algorithms with population management. In: MIC2005: The Sixth Metaheuristics International Conference, Vienna, Austria

    Google Scholar 

  11. Altntas C, Asta S, Ozcan E, Yigit T (2014) A self-generating memetic algorithm for examination timetabling. In: 10th International conference of the practice and theory of automated timetabling, pp 26–29

    Google Scholar 

  12. Ye T, Wang T, Lu Z, Hao JK (2014) A multi-parent memetic algorithm for the linear ordering problem. arXiv preprint arXiv:1405.4507

  13. Martínez-Salazar I, Molina J, Caballero R, Ángel-Bello F (2014) Memetic algorithms for solving a bi-objective transportation location routing problem. In: Proceedings of the 2014 industrial and systems engineering research conference

    Google Scholar 

  14. Dey N, Ashour AS, Nguyen GN Recent advancement in multimedia content using deep learning

    Google Scholar 

  15. Karaa WBA, Dey N (2017) Mining multimedia documents. CRC Press

    Google Scholar 

  16. Senior AW, Robinson AJ (1998) An offline cursive handwriting recognition system. IEEE Trans Pattern Anal Mach Intell 20(3):309–321

    Article  Google Scholar 

  17. Gatos B, Pratikakis I, Perantonis SJ (2006) Hybrid offline cursive handwriting word recognition. In: 18th International conference on pattern recognition (ICPR’06), pp 998–1002

    Google Scholar 

  18. Blumenstein M, Liu XY, Verma B (2007) A modified direction feature for cursive character recognition. Pattern Recogn 40(2):376–388

    Article  Google Scholar 

  19. Cheng CK, Liu XY, Blumenstein M, Marasamy VM (2004) Enhancing neural confidence based segmentation for cursive handwriting recognition. In: SEAL 04 and 2004 FIRA Robot world congress

    Google Scholar 

  20. Bozinovic RM, Shrihari SN (1989) Offline cursive script word recognition. IEEE Trans Pattern Anal Mach Intell 11(1):68–83

    Article  Google Scholar 

  21. Plamondan R, Shrihari SN (2000) Online and offline handwriting recognition: a comprehensive survey. IEEE Trans Pattern Anal Mach Intell 22(1)

    Google Scholar 

  22. Rodrigues RJ, Thome ACG (2000) Cursive character recognition—a character segmentation method using projection profile-based technique. In: 6th International Conference on Information System, Analysis and Synthesis—ISAS

    Google Scholar 

  23. Malik L, Deshpande PS, Sandhya Bhagat (2006) Character recognition using relationship between connected segments and neural network. Wseas Trans Comput 5(1)

    Google Scholar 

  24. Rehman A, Saba T (2012) Off-Line cursive script recognition: current advances, comparisons and remaining problems. Artif Intell Rev 37:261–288

    Article  Google Scholar 

  25. Verma B, Blumenstein M (2008) Pattern recognition technologies and applications: recent advances. Information Science Reference (An Imprint of IGI Global Publications), Hershey, New York, pp 1–16

    Google Scholar 

  26. Alginahi Y (2010) Preprocessing techniques in character recognition, character recognition. In: Mori M (ed) InTechopen Publishers, pp 1–20, ISBN: 978-953-307-105-3

    Google Scholar 

  27. Minimum Edit Distance, http://www.merriampark.com/ld.htm

  28. Karaa WBA, Ashour AS, Sassi DB, Roy P, Kausar N, Dey N (2016) Medline text mining: an enhancement genetic algorithm based approach for document clustering. In: Applications of intelligent optimization in biology and medicine. Springer International Publishing, pp 267–287

    Google Scholar 

  29. Smith J (2002) Genetic algorithms: simulating evolution on the computer. Part 1

    Google Scholar 

  30. Bazzoli A, Tettamanzi AGB (2004) A memetic algorithm for protein structure prediction in a 3D-lattice HP model. In: EvoWorkshop, LNCS3005, p 1

    Google Scholar 

  31. Vashist P, Hema K (2013) Character recognition with minimum edit distance method. Int J Sci Res (IJSR) 2(4) India Online ISSN: 2319‐7064

    Google Scholar 

  32. Arora S, Bhattacharjee D, Nasipuri M, Basu DK, Kundu M (2010) Recognition of non-compound handwritten devnagari characters using a combination of MLP and minimum edit distance. Int J Comput Sci Secur (IJCSS) 4(1)

    Google Scholar 

  33. Abandah GA, Jamour FT (2014) A word matching algorithm in handwritten Arabic recognition using multiple-sequence weighted edit distances. IJCSI Int J Comput Sci 11(3):18

    Google Scholar 

  34. Oncina Jose, Sebban Marc (2006) Learning stochastic edit distance: application in handwritten character recognition. Pattern Recogn 39:1575–1587 Elsevier

    Article  Google Scholar 

  35. Deshpande PS, Malik L, Arora S (2008) Fine classification & recognition of hand written devnagari characters with regular expressions & minimum edit distance method. J Comput 3(5):11–17

    Article  Google Scholar 

  36. Khurshid K, Faure C, Vincent N (2009) A novel approach for word spotting using merge-split edit distance. Laboratoire CRIP5—SIP, Université Paris Descartes, 45 rue des Saints-Pères, 75006, Paris, France

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Shri Ramdeobaba College of Engineering and Management, Nagpur, India

    Rashmi Welekar

  2. Nagpur Institute of Technology, Nagpur, India

    Nileshsingh V. Thakur

Authors
  1. Rashmi Welekar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nileshsingh V. Thakur
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Rashmi Welekar or Nileshsingh V. Thakur .

Editor information

Editors and Affiliations

  1. School of Science and Technology, Middlesex University, London, UK

    Xin-She Yang

  2. University of Reading, Reading, UK

    Simon Sherratt

  3. Department of Information Technology, Techno India College of Technology, Kolkata, West Bengal, India

    Nilanjan Dey

  4. Sabar Institute of Technology, Sabarkantha, Gujarat, India

    Amit Joshi

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

Welekar, R., Thakur, N.V. (2019). Best Bound Population-Based Local Search for Memetic Algorithm in View of Character Recognition. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Third International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 797. Springer, Singapore. https://doi.org/10.1007/978-981-13-1165-9_31

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-1165-9_31

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1164-2

  • Online ISBN: 978-981-13-1165-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation