Skip to main content
SpringerLink
Log in
Book cover

International Conference on Computational Intelligence, Communications, and Business Analytics

CICBA 2017: Computational Intelligence, Communications, and Business Analytics pp 599–613Cite as

Memetic Algorithm Based Feature Selection for Handwritten City Name Recognition

  • Conference paper
  • First Online:

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 776))

Abstract

Feature selection plays a key role to reduce the high-dimensionality of feature space in machine learning applications by discarding irrelevant and redundant features with the aim of obtaining a subset of features that accurately describe a given problem with a minimum or no degradation of performance. In this paper, a Memetic Algorithm (MA) based Wrapper-filter feature selection framework is proposed for the recognition of handwritten Bangla city names. For evaluating the MA framework, a recently published feature extraction technique, reported in [1], is used for the said pattern recognition problem. Experimentation is conducted on an in-house dataset of 6000 words written in Bangla script. Here, 40 most popular city names of West Bengal, a state in India, have been considered to prepare the dataset. Proposed technique not only reduces the feature dimension, but also enhances the performance of the word recognition technique significantly.

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

References

  1. Barua, S., Malakar, S., Bhowmik, S., Sarkar, R., Nasipuri, M.: Bangla handwritten city name recognition using gradient based feature. In: 5th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) (2016)

    Google Scholar 

  2. Narendra, P.M., Fukunaga, K.: A branch and bound algorithm for feature subset selection. IEEE Trans. Comput. 26(9), 917–922 (1977)

    Article  MATH  Google Scholar 

  3. Chen, X.W.: An improved branch and bound algorithm for feature selection. Pattern Recogn. Lett. 24(12), 1925–1933 (2003)

    Article  Google Scholar 

  4. Raymer, M.L., Punch, W.F., Goodman, E.D., Kuhn, L.A., Jain, A.K.: Dimensionality reduction using genetic algorithms. IEEE Trans. Evol. Comput. 4(2), 164–171 (2000)

    Article  Google Scholar 

  5. Oh, I.S., Lee, J.S., Moon, B.R.: Hybrid genetic algorithms for feature selection. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1424–1437 (2004)

    Article  Google Scholar 

  6. Pudil, P., Novovičová, J., Kittler, J.: Floating search methods in feature selection. Pattern Recogn. Lett. 15(11), 1119–1125 (1994)

    Article  Google Scholar 

  7. Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40(1), 16–28 (2014)

    Article  Google Scholar 

  8. Valdez, F., Melin, P., Castillo, O.: A survey on nature-inspired optimization algorithms with fuzzy logic for dynamic parameter adaptation. Expert Syst. Appl. 41(14), 6459–6466 (2014)

    Article  Google Scholar 

  9. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3((Mar)), 1157–1182 (2003)

    MATH  Google Scholar 

  10. Kotsiantis, S.: Feature selection for machine learning classification problems: a recent overview. Artif. Intell. Rev., 1–20 (2011)

    Google Scholar 

  11. Law, M.H., Figueiredo, M.A., Jain, A.K.: Simultaneous feature selection and clustering using mixture models. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1154–1166 (2004)

    Article  Google Scholar 

  12. Sánchez-Maroño, N., Alonso-Betanzos, A., Tombilla-Sanromán, M.: Filter methods for feature selection – a comparative study. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds.) IDEAL 2007. LNCS, vol. 4881, pp. 178–187. Springer, Heidelberg (2007). doi:10.1007/978-3-540-77226-2_19

    Chapter  Google Scholar 

  13. Xue, B., Zhang, M., Browne, W.N.: Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Trans. Cybern. 43(6), 1656–1671 (2013)

    Article  Google Scholar 

  14. García-Pedrajas, N., de Haro-García, A., Pérez-Rodríguez, J.: A scalable memetic algorithm for simultaneous instance and feature selection. Evol. Comput. 22(1), 1–45 (2014)

    Article  Google Scholar 

  15. Montazeri, M., Montazeri, M., Naji, H.R., Faraahi, A.: A novel memetic feature selection algorithm. In: 5th Conference on Information and Knowledge Technology (IKT), pp. 295–300. IEEE Press, New York (2013)

    Google Scholar 

  16. Yang, C.S., Chuang, L.Y., Chen, Y.J., Yang, C.H.: Feature selection using memetic algorithms. In: Third International Conference on Convergence and Hybrid Information Technology (ICCIT 2008), vol. 1, pp. 416–423. IEEE Press, New York (2008)

    Google Scholar 

  17. Cateni, S., Colla, V., Vannucci, M.: A hybrid feature selection method for classification purposes. In: European Modelling Symposium (EMS), pp. 39–44. IEEE Press, New York (2014)

    Google Scholar 

  18. Zhu, Z., Ong, Y.S., Dash, M.: Wrapper–filter feature selection algorithm using a memetic framework. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 37(1), 70–76 (2007)

    Article  Google Scholar 

  19. Roy, K., Vajda, S., Pal, U., Chaudhuri, B.B.: A system towards Indian postal automation. In: Ninth International Workshop on Frontiers in Handwriting Recognition (IWFHR-9), pp. 580–585. IEEE Press, New York (2004)

    Google Scholar 

  20. Bhowmik, S., Polley, S., Roushan, M.G., Malakar, S., Sarkar, R., Nasipuri, M.: A holistic word recognition technique for handwritten Bangla words. Int. J. Appl. Pattern Recogn. 2(2), 142–159 (2015)

    Article  Google Scholar 

  21. Bhowmik, S., Malakar, S., Sarkar, R., Nasipuri, M.: Handwritten bangla word recognition using elliptical features. In: International Conference on Computational Intelligence and Communication Networks (CICN), pp. 257–261. IEEE Press, New York (2014)

    Google Scholar 

  22. Bhowmik, S., Roushan, M.G., Sarkar, R., Nasipuri, M., Polley, S., Malakar, S.: Handwritten bangla word recognition using hog descriptor. In: Fourth International Conference of Emerging Applications of Information Technology (EAIT), pp. 193–197. IEEE Press, New York (2014)

    Google Scholar 

  23. Frinken, V., Fischer, A., Manmatha, R., Bunke, H.: A novel word spotting method based on recurrent neural networks. IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 211–224 (2012)

    Article  Google Scholar 

  24. Doermann, D.: The indexing and retrieval of document images: a survey. Comput. Vis. Image Underst. 70(3), 287–298 (1998)

    Article  Google Scholar 

  25. Manmatha, R., Han, C., Riseman, E.M.: Word spotting: a new approach to indexing handwriting. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 631–637. IEEE Press, New York (1996)

    Google Scholar 

  26. Basu, S., Das, N., Sarkar, R., Kundu, M., Nasipuri, M., Basu, D.K.: A hierarchical approach to recognition of handwritten Bangla characters. Pattern Recogn. 42(7), 1467–1484 (2009)

    Article  MATH  Google Scholar 

  27. Ishidera, E., Lucas, S.M., Downton, A.C.: Top-down likelihood word image generation model for holistic word recognition. In: Lopresti, D., Hu, J., Kashi, R. (eds.) DAS 2002. LNCS, vol. 2423, pp. 82–94. Springer, Heidelberg (2002). doi:10.1007/3-540-45869-7_11

    Chapter  Google Scholar 

  28. Acharyya, A., Rakshit, S., Sarkar, R., Basu, S., Nasipuri, M.: Handwritten word recognition using MLP based classifier: a holistic approach. Int. J. Comput. Sci. Issues 10(2), 422–427 (2013)

    Google Scholar 

  29. Malakar, S., Sharma, P., Singh, P.K., Das, M., Sarkar, R., Nasipuri, M.: A holistic approach for handwritten hindi word recognition. Int. J. Comput. Vision Image Process. (IJCVIP) 7(1), 59–78 (2017)

    Article  Google Scholar 

  30. AlKhateeb, J.H., Pauplin, O., Ren, J., Jiang, J.: Performance of hidden Markov model and dynamic Bayesian network classifiers on handwritten Arabic word recognition. Knowl.-Based Syst. 24(5), 680–688 (2011)

    Article  Google Scholar 

  31. Gao, J., Li, M., Wu, A., Huang, C.N.: Chinese word segmentation and named entity recognition: a pragmatic approach. Computational Linguistics 31(4), 531–574 (2005)

    Article  MATH  Google Scholar 

  32. Sarkar, R., Malakar, S., Das, N., Basu, S., Kundu, M., Nasipuri, M.: Word extraction and character segmentation from text lines of unconstrained handwritten Bangla document images. J. Intell. Syst. 20(3), 227–260 (2011)

    Google Scholar 

  33. Pal, U., Roy, K., Kimura, F.: A lexicon-driven handwritten city-name recognition scheme for Indian postal automation. IEICE Trans. Inf. Syst. 92(5), 1146–1158 (2009)

    Article  Google Scholar 

  34. Pal, U., Roy, R.K., Kimura, F.: Bangla and English city name recognition for Indian postal automation. In: 20th International Conference on Pattern Recognition (ICPR), pp. 1985–1988. IEEE Press, New York (2010)

    Google Scholar 

  35. Pal, U., Roy, R.K., Kimura, F.: Handwritten street name recognition for Indian postal automation. In: International Conference on Document Analysis and Recognition (ICDAR), pp. 483–487. IEEE Press, New York (2011)

    Google Scholar 

  36. Roy, P.P., Bhunia, A.K., Das, A., Dey, P., Pal, U.: HMM-based Indic handwritten word recognition using zone segmentation. Pattern Recogn. 60, 1057–1075 (2016)

    Article  Google Scholar 

  37. Pal, U., Roy, R.K., Kimura, F.: Multi-lingual city name recognition for Indian postal automation. In: International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 169–173. IEEE Press, New York (2012)

    Google Scholar 

  38. Vajda, S., Roy, K., Pal, U., Chaudhuri, B.B., Belaid, A.: Automation of Indian postal documents written in Bangla and English. Int. J. Pattern Recognit Artif Intell. 23(08), 1599–1632 (2009)

    Article  Google Scholar 

  39. Roy, P.P., Dey, P., Roy, S., Pal, U., Kimura, F.: A novel approach of Bangla handwritten text recognition using HMM. In: 14th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 661–666. IEEE Press, New York (2014)

    Google Scholar 

  40. Joarder, M.M.A., Mahmud, K., Ahmed, T., Kawser, M., Ahamed, B.: Bangla automatic number plate recognition system using artificial neural network. Asian Trans. Sci. Technol. (ATST) 2(1), 1–10 (2012)

    Google Scholar 

  41. Witkin, A.: Scale-space filtering: a new approach to multi-scale description. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 9, pp. 150–153. IEEE Press, New York (1984)

    Google Scholar 

  42. Gardner, M.W., Dorling, S.R.: Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos. Environ. 32(14), 2627–2636 (1998)

    Article  Google Scholar 

  43. De Jong, K.A., Spears, W.M.: A formal analysis of the role of multi-point crossover in genetic algorithms. Ann. Math. Artif. Intell. 5(1), 1–26 (1992)

    Article  MATH  Google Scholar 

  44. Goldberg, D.E.: Genetic algorithms. Pearson Education India

    Google Scholar 

  45. Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemometr. Intell. Lab. Syst. 2(1–3), 37–52 (1987)

    Article  Google Scholar 

  46. Robnik-Šikonja, M., Kononenko, I.: Theoretical and empirical analysis of ReliefF and RReliefF. Mach. Learn. 53(1–2), 23–69 (2003)

    Article  MATH  Google Scholar 

  47. Censor, Y.: Pareto optimality in multi objective problems. Appl. Mathe. Optim. 4(1), 41–59 (1977)

    Article  MATH  Google Scholar 

  48. Glover, F., Laguna, M.: Tabu Search. Springer, New York (2013)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Manosij Ghosh, Showmik Bhowmik, Ram Sarkar & Mita Nasipuri

  2. Department of Computer Science, Asutosh College, Kolkata, India

    Samir Malakar

Authors
  1. Manosij Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Samir Malakar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Showmik Bhowmik
    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. Mita Nasipuri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manosij Ghosh .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    J. K. Mandal

  2. Department of Computer and System Sciences, Visva Bharati University, Bolpur Santiniketan, West Bengal, India

    Paramartha Dutta

  3. Department of Information Technology, Calcutta Business School, Kolkata, India

    Somnath Mukhopadhyay

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Ghosh, M., Malakar, S., Bhowmik, S., Sarkar, R., Nasipuri, M. (2017). Memetic Algorithm Based Feature Selection for Handwritten City Name Recognition. In: Mandal, J., Dutta, P., Mukhopadhyay, S. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2017. Communications in Computer and Information Science, vol 776. Springer, Singapore. https://doi.org/10.1007/978-981-10-6430-2_47

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-6430-2_47

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6429-6

  • Online ISBN: 978-981-10-6430-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation