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OnkoGan: Bangla Handwritten Digit Generation with Deep Convolutional Generative Adversarial Networks

  • Sadeka HaqueEmail author
  • Shammi Akter Shahinoor
  • AKM Shahariar Azad RabbyEmail author
  • Sheikh Abujar
  • Syed Akhter Hossain
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1037)

Abstract

From a very early age human achieve a precious skill that is a handwriting. After this invention, the ardor of it changed day by day. And every human has a different style of handwriting. So, facsimile anyone’s handwriting is a difficult task and it needs the strong ability of brain and practice. This paper is about this mimicry where an artificial system will do this by using Generative Adversarial Networks (GANs) [1]. GANs used in unsupervised machine learning that is implemented by two neural networks. GANs has a generator which generates fake images and a discriminator which make a difference between a real image and a fake image. We trained our proposed DCGAN [2] (Deep convolutional generative adversarial networks) to achieve our goal by using the three most popular Bangla handwritten datasets CMATERdb [3], BanglaLekha-Isolated [4], ISI [5] and our own dataset Ekush [6]. The proposed DCGAN successfully generate Bangla digits which makes it a robust model to generate Bangla handwritten digits from random noise. All code and datasets are freely available on https://github.com/SadekaHaque/BanglaGan.

Keywords

DCGAN GAN Handwriting recognition Deep learning Bangla handwriting 

References

  1. 1.
    Goodfellow, I., et al.: Generative adversarial nets. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 27, pp. 2672–2680. Curran Associates Inc., Red Hook (2014)Google Scholar
  2. 2.
    Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. CoRR, abs/1511.06434 (2015)Google Scholar
  3. 3.
    Sarkar, R., Das, N., Basu, S., Kundu, M., Nasipuri, M., Basu, D.K.: CMATERdb1: a database of unconstrained handwritten Bangla and Bangla-English mixed script document image. Int. J. Doc. Anal. Recogn. (IJDAR) 15(1), 71–83 (2012)CrossRefGoogle Scholar
  4. 4.
    Biswas, M., et al.: BanglaLekha-Isolated: a multi-purpose comprehensive dataset of Handwritten Bangla Isolated characters. Data in Brief. 12, 103–107 (2017).  https://doi.org/10.1016/j.dib.2017.03.035CrossRefGoogle Scholar
  5. 5.
    Bhattacharya, U., Chaudhuri, B.: Handwritten numeral databases of indian scripts and multistage recognition of mixed numerals. IEEE Trans. Pattern Anal. Mach. Intell. 31, 444–457 (2009).  https://doi.org/10.1109/TPAMI.2008.88CrossRefGoogle Scholar
  6. 6.
    Rabby, AKM Shahariar Azad., Abujar, S., Haque, S., Hossain, S.A.: Bangla Handwritten Digit Recognition Using Convolutional Neural Network. In: Abraham, A., Dutta, P., Mandal, J.K., Bhattacharya, A., Dutta, S. (eds.) Emerging Technologies in Data Mining and Information Security. AISC, vol. 755, pp. 111–122. Springer, Singapore (2019).  https://doi.org/10.1007/978-981-13-1951-8_11Google Scholar
  7. 7.
    Ghosh, A., Bhattacharya, B., Chowdhury, S.B.R.: Hand-writing profiling using generative adversarial networks. CoRR, abs/1611.08789 (2016)Google Scholar
  8. 8.
    Islam, M.B., Azadi, M.M.B., Rahman, Md.A., Hashem, M.M.A.: Bengali handwritten character recognition using modified syntactic method. NCCPB-2005 Independent University, BangladeshGoogle Scholar
  9. 9.
    Alom, Md.Z., Sidike, P., Taha, T.M., Asari, V.: Handwritten Bangla digit recognition using deep learning (2017)Google Scholar
  10. 10.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, Lille, France, 07–09 July 2015, vol. 37, pp. 448–456. PMLR (2015)Google Scholar
  11. 11.
    Ramachandran, P., Zoph, B., Le, Q.V.: Searching for activation functions. CoRR, abs/1710.05941 (2017)Google Scholar
  12. 12.
    Xu, B., Wang, N., Chen, T., Li, M.: Empirical evaluation of rectified activations in convolutional network. CoRR, abs/1505.00853 (2015)Google Scholar
  13. 13.
    Han, J., Moraga, C.: The influence of the sigmoid function parameters on the speed of backpropagation learning. In: Mira, J., Sandoval, F. (eds.) IWANN 1995. LNCS, vol. 930, pp. 195–201. Springer, Heidelberg (1995).  https://doi.org/10.1007/3-540-59497-3_175CrossRefGoogle Scholar
  14. 14.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR, abs/1412.6980 (2014)Google Scholar
  15. 15.
    Santosh, K.C., Wendling, L.: Character recognition based on non-linear multi-projection profiles measure. Front. Comput. Sci. 9(5), 678–690 (2015)CrossRefGoogle Scholar
  16. 16.
    Deans, S.R.: Applications of the Radon Transform. Wiley Interscience Publications, New York (1983)zbMATHGoogle Scholar
  17. 17.
    Santosh, K.C.: Character recognition based on DTW-Radon. In: 11th International Conference on Document Analysis and Recognition – ICDAR 2011, Beijing, China, September 2011, pp. 264–268. IEEE Computer Society (2011).  https://doi.org/10.1109/ICDAR.2011.61. inria-00617298
  18. 18.
    Kruskall, J.B., Liberman, M.: The symmetric time warping algorithm: from continuous to discrete. In: Time Warps, String Edits and Macromolecules: The Theory and Practice of String Comparison, pp. 125–161. Addison-Wesley (1983)Google Scholar
  19. 19.
    Ukil, S., et al.: Deep learning for word-level handwritten Indic script identification. arXiv:1801.01627v1 [cs.CV], 5 January 2018

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Sadeka Haque
    • 1
    Email author
  • Shammi Akter Shahinoor
    • 1
  • AKM Shahariar Azad Rabby
    • 1
    Email author
  • Sheikh Abujar
    • 1
  • Syed Akhter Hossain
    • 1
  1. 1.Department of Computer Science and EngineeringDaffodil International UniversityDhakaBangladesh

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