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Integrating scattering feature maps with convolutional neural networks for Malayalam handwritten character recognition

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Abstract

Convolutional neural network (CNN)-based deep learning architectures are the state-of-the-art in image-based pattern recognition applications. The receptive filter fields in convolutional layers are learned from training data patterns automatically during classifier learning. There are number of well-defined, well-studied and proven filters in the literature that can extract informative content from the input patterns. This paper focuses on utilizing scattering transform-based wavelet filters as the first-layer convolutional filters in CNN architecture. The scattering networks are generated by a series of scattering transform operations. The scattering coefficients generated in first few layers are effective in capturing the dominant energy contained in the input data patterns. The present work aims at replacing the first-layer convolutional feature maps in CNN architecture with scattering feature maps. This architecture is equivalent to utilizing scattering wavelet filters as the first-layer receptive fields in CNN architecture. The proposed hybrid CNN architecture experiments the Malayalam handwritten character recognition which is one of the challenging multi-class classification problems. The initial studies confirm that the proposed hybrid CNN architecture based on scattering feature maps could perform better than the equivalent self-learning architecture of CNN on handwritten character recognition problems.

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References

  1. Trier, Ø.D., Jain, A.K., Taxt, T.: Feature extraction methods for character recognition—a survey. Pattern Recognit. 29(4), 641–662 (1996)

    Article  Google Scholar 

  2. Arica, N., Yarman-Vural, F.T.: An overview of character recognition focused on off-line handwriting. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 31(2), 216–233 (2001)

    Article  Google Scholar 

  3. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  4. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  5. Simard, P.Y., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: Seventh International Conference on Document Analysis and Recognition, pp. 958–963. IEEE (2003)

  6. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: 25th International Conference on Neural Information Processing Systems—Volume 1. NIPS’12, pp. 1097–1105 (2012)

  7. LeCun, Y., Kavukcuoglu, K., Farabet, C.: Convolutional networks and applications in vision. In: 2010 IEEE International Symposium on Circuits and Systems, pp. 253–256 (2010)

  8. Ciresan, D.C., Meier, U., Gambardella, L.M., Schmidhuber, J.: Convolutional neural network committees for handwritten character classification. In: 2011 International Conference on Document Analysis and Recognition, pp. 1135–1139. IEEE (2011)

  9. Ciresan, D.C., Meier, U., Masci, J., Maria Gambardella, L., Schmidhuber, J.: Flexible, high performance convolutional neural networks for image classification. In: International Joint Conference on Artificial Intelligence, pp. 1237–1242 (2011)

  10. Ciregan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3642–3649. IEEE (2012)

  11. Manjusha, K., Anand Kumar, M., Soman, K.P.: Scattering representation in Malayalam character recognition. In: 2017 Twenty-third National Conference on Communications (NCC), pp. 1–5 (2017)

  12. Luan, S., Zhang, B., Chen, C., Cao, X., Ye, Q., Han, J., Liu, J.: Gabor convolutional networks. arXiv preprint arXiv:1705.01450 (2017)

  13. Yang, W., Jin, L., Xie, Z., Feng, Z.: Improved deep convolutional neural network for online handwritten Chinese character recognition using domain-specific knowledge. In: 13th International Conference on Document Analysis and Recognition, pp. 551–555. IEEE (2015)

  14. Zhong, Z., Jin, L., Xie, Z.: High performance offline handwritten Chinese character recognition using GoogLeNet and directional feature maps. In: 13th International Conference on Document Analysis and Recognition, pp. 846–850. IEEE (2015)

  15. Zhang, X.-Y., Bengio, Y., Liu, C.-L.: Online and offline handwritten Chinese character recognition: a comprehensive study and new benchmark. Pattern Recognit. 61, 348–360 (2017)

    Article  Google Scholar 

  16. Perronnin, F., Larlus, D.: Fisher vectors meet neural networks: a hybrid classification architecture. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3743–3752 (2015)

  17. Ng, C.J., Teoh, A.B.J.: DCTNet: A simple learning-free approach for face recognition. In: 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, pp. 761–768. IEEE (2015)

  18. Chan, T.-H., Jia, K., Gao, S., Lu, J., Zeng, Z., Ma, Y.: PCANet: a simple deep learning baseline for image classification? IEEE Trans. Image Process. 24(12), 5017–5032 (2015)

    Article  MathSciNet  Google Scholar 

  19. Chang, S.-Y., Morgan, N.: Robust CNN-based speech recognition with Gabor filter kernels. In: Fifteenth Annual Conference of the International Speech Communication Association. INTERSPEECH, vol. 2014, pp. 905–909 (2014)

  20. Wang, Z., Liu, D., Yang, J., Han, W., Huang, T.: Deep networks for image super-resolution with sparse prior. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 370–378 (2015)

  21. Bruna, J., Mallat, S.: Invariant scattering convolution networks. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1872–1886 (2013)

    Article  Google Scholar 

  22. Nikhila, Haridas, Sowmya, V., Soman, K.P.: Comparative analysis of scattering and random features in hyperspectral image classification. Proc. Comput. Sci. 58, 307–314 (2015)

    Article  Google Scholar 

  23. Sifre, L., Mallat, S.: Rotation, scaling and deformation invariant scattering for texture discrimination. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1233–1240 (2013)

  24. Pal, U., Chaudhuri, B.B.: Indian script character recognition: a survey. Pattern Recognit. 37(9), 1887–1899 (2004)

    Article  Google Scholar 

  25. Govindaraju, V., Setlur, S.: Guide to OCR for Indic Scripts. Springer, London (2009)

    Google Scholar 

  26. Rahiman, M.A., Rajasree, M.S.: Printed Malayalam character recognition using back-propagation neural networks. In: 2009 IEEE International Advance Computing Conference, pp. 197–201. IEEE (2009)

  27. John, J., Pramod, K.V., Balakrishnan, K.: Unconstrained handwritten Malayalam character recognition using wavelet transform and support vector machine classifier. Proc. Eng. 30, 598–605 (2012)

    Article  Google Scholar 

  28. Manjusha, K., Anand Kumar, M., Soman, K.P.: Experimental analysis on character recognition using singular value decomposition and random projection. Int. J. Eng. Technol. 7(4), 1246–1255 (2015)

    Google Scholar 

  29. Kumar, S.S., Manjusha, K., Soman, K.P.: Novel SVD Based Character Recognition Approach for Malayalam Language Script. In: Thampi, S., Abraham, A., Pal, S., Rodriguez, J. (eds) Recent Advances in Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 235. Springer, Cham (2014)

  30. Neeba, N.V., Jawahar, C.V.: Empirical evaluation of character classification schemes. In: 2009 Seventh International Conference on Advances in Pattern Recognition, pp. 310–313. IEEE Computer Society (2009)

  31. Shivsubramani, K., Loganathan, R., Srinivasan, C.J., Ajay, V., Soman, K.P.: Multiclass hierarchical SVM for recognition of printed Tamil characters. In: International Joint Conference on Artificial Intelligence 2007 Workshop on Analytics for Noisy Unstructured Text Data (2007)

  32. Mallat, S.: Group invariant scattering. Commun. Pure Appl. Math. 65(10), 1331–1398 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  33. LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time series. In: Arbib, M.A. (ed.) The Handbook of Brain Theory and Neural Networks, pp. 255–258. MIT Press, Cambridge (1998)

    Google Scholar 

  34. Oyallon, E., Belilovsky, E., Zagoruyko, S.: Scaling the scattering transform: deep hybrid networks. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 5619–5628 (2017)

  35. Goldstein, T., Bresson, X., Osher, S.: Geometric applications of the split Bregman method: segmentation and surface reconstruction. J. Sci. Comput. 45(1–3), 272–293 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  36. Soman, K .P., Ramanathan, R.: Digital Signal and Image Processing—The Sparse Way, 1st edn. Elsevier India, Gurgaon (2012)

    Google Scholar 

  37. Syama, K., George, N., Swathy Sekhar, C.S., Neethu ,M., Manikandan, S., Soman, K.P.: Performance study of active contour model based character segmentation with nonlinear diffusion. In: International Conference on Advances in Computing and Communications (ICACC), pp. 118–121. IEEE (2012)

  38. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  MathSciNet  Google Scholar 

  39. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S. Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). Software available from tensorflow.org

  40. Chollet, F., et al.: Keras. https://keras.io (2015). Accessed 15 Dec 2017

  41. Sifre, L., Anden, J., Kapoko, M., Oyallon, E., Lostanlen, V.: ScatNet: a MATLAB toolbox for scattering networks (2013)

  42. Manjusha, K., Anand Kumar, M., Soman, K.P.: Reduced scattering representation for Malayalam character recognition. Arab. J. Sci. Eng. (2017). https://doi.org/10.1007/s13369-017-2945-9

    Google Scholar 

  43. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol 1, pp. 886–893. IEEE (2005)

  44. Chang, C.-C., Lin, C.-J.: Libsvm a library for support vector machines. ACM Trans. Intell. Syst. Technol. 3(2), 27:1–27:27 (2011)

    Google Scholar 

  45. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

  46. MNIST competition tensorflow KR group. https://github.com/kkweon/mnist-competition. Accessed On 20 Jan (2018)

  47. Bhattacharya, U., Chaudhuri, B.B.: Databases for research on recognition of handwritten characters of Indian scripts. In Eighth International Conference on Document Analysis and Recognition, vol. 2, pp 789–793. IEEE (2005)

  48. Liu, C.-L., Yin, F., Wang, D.-H., Wang, Q.-F.,: CASIA online and offline Chinese handwriting databases. In: 2011 International Conference on Document Analysis and Recognition, pp. 37–41. IEEE (2011)

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Authors and Affiliations

  1. Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Coimbatore, India

    K. Manjusha, M. Anand Kumar & K. P. Soman

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  1. K. Manjusha
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  2. M. Anand Kumar
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  3. K. P. Soman
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Correspondence to K. Manjusha.

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Manjusha, K., Anand Kumar, M. & Soman, K.P. Integrating scattering feature maps with convolutional neural networks for Malayalam handwritten character recognition. IJDAR 21, 187–198 (2018). https://doi.org/10.1007/s10032-018-0308-z

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  • DOI: https://doi.org/10.1007/s10032-018-0308-z

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