Feature Selection for Handwritten Word Recognition Using Memetic Algorithm

  • Manosij Ghosh
  • Samir Malakar
  • Showmik Bhowmik
  • Ram Sarkar
  • Mita Nasipuri
Part of the Studies in Computational Intelligence book series (SCI, volume 687)


Nowadays, feature selection is considered as a de facto standard in the field of pattern recognition where high-dimensional feature attributes are used. The main purpose of any feature selection algorithm is to reduce the dimensionality of the input feature vector while improving the classification ability. Here, a Memetic Algorithm (MA)-based wrapper–filter feature selection method is applied for the recognition of handwritten word images in segmentation-free approach. In this context, two state-of-the-art feature vectors describing texture and shape of the word images, respectively, are considered for feature dimension reduction. Experimentation is conducted on handwritten Bangla word samples comprising 50 popular city names of West Bengal, a state of India. Final results confirm that for the said recognition problem, subset of features selected by MA produces increased recognition accuracy than the individual feature vector or their combination when applied entirely.


Feature selection Memetic algorithm Wrapper–filter method Handwritten word recognition Bangla script City name recognition 


  1. 1.
    Ghosh, M., Malakar, S., Bhowmik, S., Sarkar, R., Nasipuri, M.: Memetic algorithm based feature selection for handwritten city name recognition. In: Proceedings of First International Conference on Computational Intelligence, Communications, and Business Analytics (2017)Google Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Sánchez-Maroño, N., Alonso-Betanzos, A., Tombilla-Sanromán, M.: Filter methods for feature selection–a comparative study. In: International Conference on Intelligent Data Engineering and Automated Learning, pp. 178–187. Springer, Heidelberg (2007)Google Scholar
  4. 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)CrossRefGoogle Scholar
  5. 5.
    Dey, N., Ashour, A.S., Beagum, S., Pistola, D.S., Gospodinov, M., Gospodinova, E.P., Tavares, J.M.R.: Parameter optimization for local polynomial approximation based intersection confidence interval filter using genetic algorithm: an application for brain MRI image de-noising. J. Imaging 1(1), 60–84 (2015)CrossRefGoogle Scholar
  6. 6.
    Karaa, W.B.A., Ashour, A.S., Sassi, D.B., Roy, P., Kausar, N., Dey, N.: MEDLINE text mining: an enhancement genetic algorithm based approach for document clustering. In: Applications of Intelligent Optimization in Biology and Medicine, pp. 267–287. Springer International Publishing (2016)Google Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    Wang, D., He, T., Li, Z., Cao, L., Dey, N., Ashour, A. S., Shi, F.: Image feature-based affective retrieval employing improved parameter and structure identification of adaptive neuro-fuzzy inference system. Neural Comput. Appl. 1–16 (2016)Google Scholar
  9. 9.
    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)CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    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, vol, 1, pp. 416–423. IEEE Press, New York (2008)Google Scholar
  12. 12.
    Cateni, S., Colla, V., Vannucci, M.: A hybrid feature selection method for classification purposes. In: European Modelling Symposium. pp. 39–44. IEEE Press, New York (2014)Google Scholar
  13. 13.
    Zhu, Z., Ong, Y.S., Dash, M.: Wrapper–filter feature selection algorithm using a memetic framework. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 37(1), 70–76 (2007)CrossRefGoogle Scholar
  14. 14.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3(Mar), 1157–1182 (2003)Google Scholar
  15. 15.
    Robnik-Šikonja, M., Kononenko, I.: Theoretical and empirical analysis of ReliefF and RReliefF. Mach. Learn. 53(1–2), 23–69 (2003)CrossRefzbMATHGoogle Scholar
  16. 16.
    Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)CrossRefGoogle Scholar
  17. 17.
    Yu, L., Liu, H.: Efficient feature selection via analysis of relevance and redundancy. Journal of machine learning research. 5(Oct), 1205–1224(2004)Google Scholar
  18. 18.
    Chu, W.S., De la Torre, F., Cohn, J.F., Messinger, D.S.: A branch-and-bound framework for unsupervised common event discovery. Int. J. Comput. Vis. 123(3), 372–391 (2017)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Aghdam, M.H., Ghasem-Aghaee, N., Basiri, M.E.: Text feature selection using ant colony optimization. Expert Syst. Appl. 36(3), 6843–6853 (2009)CrossRefGoogle Scholar
  20. 20.
    Kannan, S.S., Ramaraj, N.: A novel hybrid feature selection via symmetrical uncertainty ranking based local memetic search algorithm. Knowl. Based Syst. 23(6), 580–585 (2010)CrossRefGoogle Scholar
  21. 21.
    Zhu, Z., Ong, Y. S.: Memetic algorithms for feature selection on microarray data. Adv. Neural Netw. 1327–1335(2007)Google Scholar
  22. 22.
    Chen, Y., Abraham, A., Yang, B.: Feature selection and classification using flexible neural tree. Neurocomputing. 70(1), 305–313 (2006)CrossRefGoogle Scholar
  23. 23.
    Lee, J., Kim, D.W.: Memetic feature selection algorithm for multi-label classification. Inf. Sci. 293, 80–96 (2015)CrossRefGoogle Scholar
  24. 24.
    García, S., Cano, J.R., Herrera, F.: A memetic algorithm for evolutionary prototype selection: a scaling up approach. Pattern Recogn. 41(8), 2693–2709 (2008)CrossRefzbMATHGoogle Scholar
  25. 25.
    Guillén, A., Pomares, H., González, J., Rojas, I., Valenzuela, O., Prieto, B.: Parallel multiobjective memetic RBFNNs design and feature selection for function approximation problems. Neurocomputing 72(16), 3541–3555 (2009)CrossRefGoogle Scholar
  26. 26.
    Hu, Z., Bao, Y., Chiong, R., Xiong, T.: Mid-term interval load forecasting using multi-output support vector regression with a memetic algorithm for feature selection. Energy. 84, 419–431 (2015)CrossRefGoogle Scholar
  27. 27.
    UCI Machine Learning Repository.
  28. 28.
  29. 29.
    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)CrossRefzbMATHGoogle Scholar
  30. 30.
    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), pp. 343–352. Springer, Singapore (2017)Google Scholar
  31. 31.
    Malakar, S., Sharma, P., Singh, P.K., Das, M., Sarkar, R., Nasipuri, M.: A holistic approach for handwritten hindi word recognition. Int. J. Comput. Vis. Image Process. (IJCVIP). 7(1), 59–78 (2017)CrossRefGoogle Scholar
  32. 32.
    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 Recognit. 2(2), 142–159 (2015)CrossRefGoogle Scholar
  33. 33.
    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
  34. 34.
    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
  35. 35.
    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)CrossRefGoogle Scholar
  36. 36.
    Roy, K., Vajda, S., Pal, U., Chaudhuri, B. B.: A system towards Indian postal automation. In: Ninth International Workshop on Frontiers in Handwriting Recognition, pp. 580–585. IEEE Press, New York (2004)Google Scholar
  37. 37.
    Dzuba, G., Filatov, A., Gershuny, D., Kil, I., Nikitin, V.: Check amount recognition based on the cross validation of courtesy and legal amount fields. Int. J. Pattern Recognit Artif Intell. 11(04), 639–655 (1997)CrossRefGoogle Scholar
  38. 38.
    Roy, P.P., Bhunia, A.K., Das, A., Dhar, P., Pal, U.: Keyword spotting in doctor’s handwriting on medical prescriptions. Expert Syst. Appl. 76, 113–128 (2017)CrossRefGoogle Scholar
  39. 39.
    Languages with at least 50 million first-language speakers. Retrieved from Summary by language size Ethnologue.
  40. 40.
    Tamen, Z., Drias, H., Boughaci, D.: An efficient multiple classifier system for Arabic handwritten words recognition. Pattern Recognit. Lett. 93, 123–132 (2017)CrossRefGoogle Scholar
  41. 41.
    Hemalatha, S., Anouncia, S.M.: Unsupervised Segmentation of Remote Sensing Images using FD Based Texture Analysis Model and ISODATA. Int. J. Ambient Comput. Intell. (IJACI) 8(3), 58–75 (2017)CrossRefGoogle Scholar
  42. 42.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893. IEEE (2005)Google Scholar
  43. 43.
    Dey, N., Ashour, A.S., Hassanien, A.E.: Feature detectors and descriptors generations with numerous images and video applications: a recap. In: Feature Detectors and Motion Detection in Video Processing, IGI Global, pp. 36–65 (2017)Google Scholar
  44. 44.
    Gonzalez, R.C.: Digital Image Processing. Pearson Education, India (2009)Google Scholar
  45. 45.
    Freeman, H.: On the encoding of arbitrary geometric configurations. IRE Trans. Electron. Comput. 10, 260–268 (1961)MathSciNetCrossRefGoogle Scholar
  46. 46.
    Ong, Y.S., Keane, A.J.: Meta-Lamarckian learning in memetic algorithms. IEEE Trans. Evol. Comput. 8(2), 99–110 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Manosij Ghosh
    • 1
  • Samir Malakar
    • 2
  • Showmik Bhowmik
    • 1
  • Ram Sarkar
    • 1
  • Mita Nasipuri
    • 1
  1. 1.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia
  2. 2.Department of Computer ScienceAsutosh CollegeKolkataIndia

Personalised recommendations