Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Feature Selection for Recognition of Online Handwritten Bangla Characters

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Feature selection through optimization techniques provides an interesting approach to minimize computational time with enhanced prediction capability, and has better cognizance of data in any pattern recognition application. This paper is an extended version of previously published work (Sen et al. in: 7th IAPR TC3 workshop on artificial neural networks in pattern recognition, Ulm, Germany, pp 246–256, 2016), where a quad-tree based image segmentation approach has been discussed to estimate some topological and shape based features (192-attributed) for the recognition of online handwritten Bangla characters. The previous work achieved a recognition accuracy of 98.5% on a database consisting of 10,000 handwritten Bangla characters. In this paper, parameters, used in the previous version during feature estimation, are tuned to improve the performance of the overall system. Thereafter, krill-herd a bio-inspired, meta-heuristic algorithm has been applied to find the optimal feature vector by reducing the dimension of the original feature vector. The reduced feature vector has been fed to Sequential Minimal Optimization classifier for the recognition of the same online handwritten Bangla character database used in previous work. It has been observed that result obtained with this optimal feature set is almost equivalent as the result produced by the entire feature vector.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Srihari S, Huang C, Srinivasan H (2008) On the discriminability of the handwriting of twins. J Forensic Sci 3(2):430–446

    Article  Google Scholar 

  2. Brahnam B, Nanni L (2009) High performance set of features for human action classification. In: International conference on image processing, computer vision & pattern recognition, pp 980–984

  3. Barbu T (2010) Gabor filter-based face recognition technique. Proc Rom Acad Ser A 11(3):277–83

    MathSciNet  Google Scholar 

  4. Gupta S, Jaafar J, Wan Ahmad WF (2012) Static hand gesture recognition using local gabor filter. Procedia Eng 41:827–832

    Article  Google Scholar 

  5. Ghosh M, Adhikary S, Ghosh KK, Sardar A, Begum S, Sarkar R (2018) Genetic algorithm based cancerous gene identification from microarray data using ensemble of filter methods. Med Biol Eng Comput 57:1–18

    Google Scholar 

  6. Ghosh M, Begum S, Sarkar R, Chakraborty D, Maulik U (2009) Recursive memetic algorithm for gene selection in microarray data. Int J Expert Syst 116:172–185

    Article  Google Scholar 

  7. Sen SP, Mitra M, Chowdhury S, Sarkar R, Roy K (2016) Quad-tree based image segmentation and feature extraction to recognize online handwritten Bangla characters. In: 7th IAPR TC3 workshop on artificial neural networks in pattern recognition, Ulm, Germany, pp 246–256

    Chapter  Google Scholar 

  8. Sen SP, Sarkar R, Roy K, Hori N (2016) Recognize online handwritten Bangla characters using Hausdorff distance based feature. In: 5th international conference on frontiers in intelligent computing: theory and application, pp 541–549

    Google Scholar 

  9. Roy K, Sharma N, Pal U (2007) Online Bangla handwriting recognition system. In: International conference on advances in pattern recognition, pp 117–122

  10. Sen SP, Sarkar R, Roy K (2015) A simple and effective technique for online handwritten Bangla character recognition. In: 4th international conference on frontiers in intelligent computing: theory and application, pp 201–209

    Google Scholar 

  11. Parui SK, Guin K, Bhattacharya U, Chaudhuri BB (2008) Online handwritten Bangla character recognition using HMM. In: International conference on pattern recognition, pp 1–4

  12. Bhattacharya U, Gupta BK, Parui SK (2007) Direction code based features for recognition of online handwritten characters of Bangla. In: International conference on document analysis and recognition, pp 58–62

  13. Ghosh R (2015) A novel feature extraction approach for online Bengali and Devanagari character recognition. In: International conference on signal processing and integrated networks, pp 483–488

  14. De Stefano C, Fontanella F, Marrocco C, di Freca A Scotto (2014) A GA-based feature selection approach with an application to handwritten character recognition. Pattern Recognit Lett 35:130–141

    Article  Google Scholar 

  15. Xue B, Zhang M, Browne WN, Yao X (2016) A survey on evolutionary computation approachesto feature selection. IEEE Trans Evolut Comput 20:606–626

    Article  Google Scholar 

  16. Dash M, Liu H (1997) Feature selection for classification. Int J Intell Data Anal 1:131–156

    Article  Google Scholar 

  17. Oh IS, Lee JS, Moon BR (2004) Hybrid genetic algorithms for feature selection. IEEE Trans Pattern Anal Mach Intell 26(11):1424–1437

    Article  Google Scholar 

  18. Huang CL, Wang CJ (2006) A GA-based feature selection and parameters optimization for support vector machines. Expert Syst Appl 31:231–240

    Article  Google Scholar 

  19. Liwicki M, Bunke H (2009) Feature selection for HMM and BLSTM based handwriting recognition of whiteboard notes. Int J Pattern Recognit Artif Intell 23:907–923

    Article  Google Scholar 

  20. Kimura Y, Suzuki A, Odaka K (2009) Feature selection for character recognition using genetic algorithm. In: International conference on innovative computing, information and control (ICICIC), Kaohsiung, pp 401–404

  21. Oliveira LS, Benahmed N, Sabourin R, Bortolozzi F, Suen CY (2001) Feature subset selection using genetic algorithms for handwritten digit recognition. In: Proceedings of 14th Brazilian symposium on computer graphics and image processing, Florianopolis, Brazil, pp 362–369

  22. Oliveira LS, Sabourin R, Bortolozzi F, Suen CY (2002) Feature selection using multi-objective genetic algorithms for handwritten digit recognition. In: Proceedings of 16th international conference on pattern recognition, vol 1, pp 568–571

  23. Chandrashekar G, Sahin F (2014) A survey on feature selection methods. J Comput Electr Eng 40:16–28

    Article  Google Scholar 

  24. Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17:4831–4845

    Article  MathSciNet  Google Scholar 

  25. Kowalski PA, Lukasik S (2016) Training neural networks with krill herd algorithm. Neural Process Lett 44:5–17

    Article  Google Scholar 

  26. Wang G, Guo L, Gandomi AH, Alavi AH, Duan H (2013) Simulated annealing-based krill herd algorithm for global optimization. In: Abstract and applied analysis, Article ID 213853

    MathSciNet  MATH  Google Scholar 

  27. Sur C, Shukla A (2014) Discrete krill herd algorithm—a bio-inspired meta-heuristics for graph based network route optimization. In: 10th international conference on distributed computing and internet technology, pp 152–163

  28. Rodrigues D, Pereira LAM, Papa JP, Weber SAT (2014) A binary krill herd approach for feature selection. In: 22th international conference on pattern recognition, pp 1407–1412

  29. Rios LM, Sahinidis NV (2013) Derivative-free optimization: a review of algorithms and comparison of software implementations. J Glob Optim 56(3):1247–1293

    Article  MathSciNet  Google Scholar 

  30. Kramer O, Ciaurri DE, Koziel S (2011) Derivative-free optimization. In: Computational optimization, methods and algorithms, pp 61–83

    Chapter  Google Scholar 

  31. Krill (2012) http://en.wikipedia.org/wiki/Krill. Accessed 10 Jan 2017

  32. Hofmann EE, Haskell AGE, Klinck JM, Lascara CM (2004) Lagrangian modelling studies of Antarctic krill (Euphausia superba) swarm formation. ICES J Mar Sci 61:617–631

    Article  Google Scholar 

  33. Price HJ (1989) Swimming behaviour of krill in response to algal patches: a mesocosm study. Limnol Oceanogr 34:649–659

    Article  Google Scholar 

  34. Singh PK, Sarkar R, Das N, Basu S, Nasipuri M (2014) Statistical comparison of classifiers for script identification from multi-script handwritten documents. Int J Appl Pattern Recognit 1(2):152–172

    Article  Google Scholar 

  35. Saremi S, Mirjalili SM, Mirjalili S (2014) Chaotic krill herd optimization algorithm. In: 7th international conference interdisciplinarity in engineering, pp 180–185

  36. Okubo A (1986) Dynamical aspects of animal grouping: swarms, schools, flocks, and herds. Adv Biophys 22:1–94

    Article  Google Scholar 

  37. Zang H, Zhang S, Hapeshi K (2010) A review of nature-inspired algorithms. J Bionic Eng 7:S232–S237

    Article  Google Scholar 

  38. Morin A, Okubo A, Kawasaki K (1988) Acoustic data analysis and models of krill spatial distribution. In: Scientific committee for the Conservation of Antarctic Marine Living Resources, selected scientific papers, part I, pp 311–329

  39. Alpaydin E (2004) Introduction to machine learning. The MIT Press, Cambridge

    MATH  Google Scholar 

  40. Law MHC, Figueiredo MAT, Jain AK (2004) Simultaneous feature selection and clustering using mixture models. IEEE Trans Pattern Anal Mach Intell 26:1154–1166

    Article  Google Scholar 

  41. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evolut Comput 1:67–82

    Article  Google Scholar 

  42. Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Future Institute of Engineering and Management, Kolkata, 700150, India

    Shibaprasad Sen, Mridul Mitra & Ankan Bhattacharyya

  2. Jadavpur University, Kolkata, 700032, India

    Ram Sarkar

  3. University of Ulm, Institute of Neural Information Processing, 89069, Ulm, Germany

    Friedhelm Schwenker

  4. West Bengal State University, Barasat, Kolkata, 700126, West Bengal, India

    Kaushik Roy

Authors
  1. Shibaprasad Sen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mridul Mitra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ankan Bhattacharyya
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ram Sarkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Friedhelm Schwenker
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kaushik Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ram Sarkar.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sen, S., Mitra, M., Bhattacharyya, A. et al. Feature Selection for Recognition of Online Handwritten Bangla Characters. Neural Process Lett 50, 2281–2304 (2019). https://doi.org/10.1007/s11063-019-10010-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-019-10010-2

Keywords

Search

Navigation