Skip to main content

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

Part of the Communications in Computer and Information Science book series (CCIS,volume 1037)

  • The original version of this chapter was revised: The names of the two Authors have been corrected as “AKM Shahariar Azad Rabby” and “Syed Akhter Hossain”. The correction to this chapter is available at


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


  • GAN
  • Handwriting recognition
  • Deep learning
  • Bangla handwriting

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-981-13-9187-3_10
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   99.00
Price excludes VAT (USA)
  • ISBN: 978-981-13-9187-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   129.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.

Change history

  • 17 August 2019

    In the originally published version, the names of the two Authors on pages 108, 149, and 159 were incorrect. The names have been corrected as “AKM Shahariar Azad Rabby” and “Syed Akhter Hossain”.


  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. Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. CoRR, abs/1511.06434 (2015)

    Google Scholar 

  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)

    CrossRef  Google Scholar 

  4. Biswas, M., et al.: BanglaLekha-Isolated: a multi-purpose comprehensive dataset of Handwritten Bangla Isolated characters. Data in Brief. 12, 103–107 (2017).

    CrossRef  Google Scholar 

  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).

    CrossRef  Google Scholar 

  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).

    Google Scholar 

  7. Ghosh, A., Bhattacharya, B., Chowdhury, S.B.R.: Hand-writing profiling using generative adversarial networks. CoRR, abs/1611.08789 (2016)

    Google Scholar 

  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, Bangladesh

    Google Scholar 

  9. Alom, Md.Z., Sidike, P., Taha, T.M., Asari, V.: Handwritten Bangla digit recognition using deep learning (2017)

    Google Scholar 

  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. Ramachandran, P., Zoph, B., Le, Q.V.: Searching for activation functions. CoRR, abs/1710.05941 (2017)

    Google Scholar 

  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. 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).

    CrossRef  Google Scholar 

  14. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR, abs/1412.6980 (2014)

    Google Scholar 

  15. Santosh, K.C., Wendling, L.: Character recognition based on non-linear multi-projection profiles measure. Front. Comput. Sci. 9(5), 678–690 (2015)

    CrossRef  Google Scholar 

  16. Deans, S.R.: Applications of the Radon Transform. Wiley Interscience Publications, New York (1983)

    MATH  Google Scholar 

  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). inria-00617298

  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. Ukil, S., et al.: Deep learning for word-level handwritten Indic script identification. arXiv:1801.01627v1 [cs.CV], 5 January 2018

Download references

Author information

Authors and Affiliations


Corresponding authors

Correspondence to Sadeka Haque or AKM Shahariar Azad Rabby .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Haque, S., Shahinoor, S.A., Rabby, A.S.A., Abujar, S., Hossain, S.A. (2019). OnkoGan: Bangla Handwritten Digit Generation with Deep Convolutional Generative Adversarial Networks. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1037. Springer, Singapore.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9186-6

  • Online ISBN: 978-981-13-9187-3

  • eBook Packages: Computer ScienceComputer Science (R0)