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.
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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”.
References
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)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. CoRR, abs/1511.06434 (2015)
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)
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.2017.03.035
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.2008.88
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_11
Ghosh, A., Bhattacharya, B., Chowdhury, S.B.R.: Hand-writing profiling using generative adversarial networks. CoRR, abs/1611.08789 (2016)
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
Alom, Md.Z., Sidike, P., Taha, T.M., Asari, V.: Handwritten Bangla digit recognition using deep learning (2017)
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)
Ramachandran, P., Zoph, B., Le, Q.V.: Searching for activation functions. CoRR, abs/1710.05941 (2017)
Xu, B., Wang, N., Chen, T., Li, M.: Empirical evaluation of rectified activations in convolutional network. CoRR, abs/1505.00853 (2015)
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_175
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR, abs/1412.6980 (2014)
Santosh, K.C., Wendling, L.: Character recognition based on non-linear multi-projection profiles measure. Front. Comput. Sci. 9(5), 678–690 (2015)
Deans, S.R.: Applications of the Radon Transform. Wiley Interscience Publications, New York (1983)
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
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)
Ukil, S., et al.: Deep learning for word-level handwritten Indic script identification. arXiv:1801.01627v1 [cs.CV], 5 January 2018
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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. https://doi.org/10.1007/978-981-13-9187-3_10
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