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An Adaptive Approach of Tamil Character Recognition Using Deep Learning with Big Data-A Survey

  • R. Jagadeesh Kannan
  • S. Subramanian
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 337)

Abstract

Deep learning is currently an extremely active research area in machine learning and pattern recognition society. It has gained huge successes in a broad area of applications such as speech recognition, computer vision, and natural language processing. With the sheer size of data available today, big data brings big opportunities and transformative potential for various sectors; on the other hand, it also presents unprecedented challenges to harnessing data and information. As the data keeps getting bigger, deep learning is coming to play a key role in providing big data predictive analytics solutions. This paper presents a brief overview of deep learning and highlight how it can be effectively applied for optical character recognition in Tamil language.

Keywords

Classifier Design and Evaluation Feature Representation Machine Learning Neural Nets Models Parallel Processing Deep Learning Big Data GPGPU and Optical Character Recognition 

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References

  1. 1.
    Siromoney, G., Chandrasekaran, R., Chandrasekaran, M.: Computer recognition of printed Tamil character. Pattern Recognition 10, 243–247 (1978)CrossRefMATHGoogle Scholar
  2. 2.
    Chinnuswamy, P., Krishnamoorthy, S.G.: Recognition of hand printed Tamil characters. Pattern Recognit. 12, 141–152 (1980)CrossRefGoogle Scholar
  3. 3.
    Suresh, R.M., Ganesan, L.: Recognition of hand printed Tamil characters using classification approach. In: ICAPRDT, Kolkata, pp. 63–84 (1999)Google Scholar
  4. 4.
    Hewavitharana, S., Fernando, H.C.: A two stage classification approach to Tamil handwriting recognition. In: Tamil Internet 2002, California, USA, pp. 118–124 (2002)Google Scholar
  5. 5.
    Bhattacharya, U., Ghosh, S.K., Parui, S.K.: A two stage recognition scheme for handwritten Tamil characters. In: Proceedings of the Ninth International Conference on Document Analysis And Recognition (ICDAR 2007), pp. 511–515. IEEE Computer Society, Washington, DC (2007)CrossRefGoogle Scholar
  6. 6.
    Sutha, J., Ramaraj, N.: Neural network based offline Tamil handwritten character recognition system. In: Proceedings of the International Conference on Computational Intelligence and Multimedia Applications, vol. 2, pp. 446–450. IEEE Computer Society, Washington, DC (2007)Google Scholar
  7. 7.
    Shanthi, N., Duraiswamy, K.: Preprocessing algorithms for the recognition of Tamil handwritten characters. In: Third International CALIBER 2005, Kochi, pp. 77–82 (2005)Google Scholar
  8. 8.
    Casey, R.G., Lecolinet, E.: A survey of methods and strategies in character segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 18(7), 690–706 (1996)CrossRefGoogle Scholar
  9. 9.
    Plamondon, R., Srihari, S.N.: On-line and off-line handwriting recognition: a comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 63–84 (2000)CrossRefGoogle Scholar
  10. 10.
    Hinton, G., Salakhutdinov, R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)CrossRefMATHMathSciNetGoogle Scholar
  11. 11.
    Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009)CrossRefMATHMathSciNetGoogle Scholar
  12. 12.
    Nair, V., Hinton, G.: 3D object recongition with deep belief nets. In: Proc. Adv. NIPS, vol. 22, pp. 1339–1347 (2009)Google Scholar
  13. 13.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  14. 14.
    Rumelhart, D., Hinton, G., Williams, R.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)CrossRefGoogle Scholar
  15. 15.
    Cirean, D.C., Meier, U., Masci, J., Gambardella, L.M., Schmidhuber, J.: Flexible, high performance convolutional neural networks for image classification. In: Proc. 22nd Int. Conf. Artif. Intell., pp. 1237–1242 (2011)Google Scholar
  16. 16.
    Scherer, D., Müller, A., Behnke, S.: Evaluation of pooling operations in convolutional architectures for object recognition. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010, Part III. LNCS, vol. 6354, pp. 92–101. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    CUDA C Programming Guide, PG-02829-001_v5.5, NVIDIA Corporation, Santa Clara, CA, USA (July 2013)Google Scholar
  18. 18.
    Le, Q., et al.: Building high-level features using large scale unsupervised learning. In: Proc. Int. Conf. Mach. Learn. (2012)Google Scholar
  19. 19.
    Bottou, L.: Online algorithms and stochastic approximations. In: Saad, D. (ed.) On-Line Learning in Neural Networks. Cambridge Univ. Press, Cambridge (1998)Google Scholar
  20. 20.
    Blum, A., Burch, C.: On-line learning and the metrical task system problem. In: Proc. 10th Annu. Conf. Comput. Learn. Theory, pp. 45–53 (1997)Google Scholar
  21. 21.
    Tellache, M., Sid Ahmed, M.A., Abaza, B.: Thinning Algorithms for Arabic OCR. In: IEEE Pacific Rim Conference on Communications, Computers and Signal Processing 1993, May 19-21, vol. 1, pp. 248–251 (1993)Google Scholar
  22. 22.
    Kwon, J.-S., Gi’, J.-W., Kang, E.-K.: An enhanced thinning algorithm using parallel processing. In: Proceedings of the 2001 International Conference Image Processing, October 7-10, vol. 3, pp. 752–755 (2001)Google Scholar
  23. 23.
    Crego, E., Munoz, G., Islam, F.: Big data and deep learning: Big deals or big delusions? Business (2013), http://www.hufngtonpost.com/george-munoz-frank-islamand-ed-crego/big-data-and-deep-learnin_b_3325352.html
  24. 24.
    Bengio, Y., Bengio, S.: Modeling high-dimensional discrete data with multi-layer neural networks. In: Proc. Adv. Neural Inf. Process. Syst., vol. 12, pp. 400–406 (2000)Google Scholar
  25. 25.
    Marc’Aurelio Ranzato, Y., Boureau, L., LeCun, Y.: Sparse feature learning for deep belief networks. In: Proc. Adv. Neural Inf. Process. Syst., vol. 20, pp. 1185–1192 (2007)Google Scholar
  26. 26.
    Dahl, G.E., Yu, D., Deng, L., Acero, A.: Context-dependent pretrained deep neural networks for large-vocabulary speech recognition. IEEE Trans. Audio, Speech, Lang. Process. 20(1), 30–41 (2012)CrossRefGoogle Scholar
  27. 27.
    Hinton, G., et al.: Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Process. Mag. 29(6), 82–97 (2012)CrossRefMathSciNetGoogle Scholar
  28. 28.
    Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted Boltzmann machines for collaborative _ltering. In: Proc. 24th Int. Conf. Mach. Learn., pp. 791–798 (2007)Google Scholar
  29. 29.
    Cirean, D., Meler, U., Cambardella, L., Schmidhuber, J.: Deep, big, simple neural nets for handwritten digit recognition. Neural Comput. 22(12), 3207–3220 (2010)CrossRefGoogle Scholar
  30. 30.
    Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing almost from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)MATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • R. Jagadeesh Kannan
    • 1
  • S. Subramanian
    • 2
  1. 1.VIT UniversityChennaiIndia
  2. 2.R.M.D Engineering CollegeChennaiIndia

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