Abstract
Many machine learning applications require training datasets containing enormous amounts of data. Generating such huge amounts of data is quite a formidable task in machine learning tasks such as handwritten character recognition. Generative models are emerging as machines having human-like cognitive abilities. They are helpful in augmenting the handwritten character datasets. A deep convolution generative adversarial network (DCGAN) has been developed in this study to generate Devanagari characters. The characters generated by the DCGAN would augment the handwritten character dataset used in training and testing learning machines for character recognition applications. The DCGAN has been tested for numerical and characters of the Devanagari script. The details of the developed DCGAN and those of its implementation have been presented in this paper. The results show that images generated by the DCGAN closely resemble with the handwritten characters.
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Nannapaneni, R., Chakravarti, A., Sangappa, S., Bora, P., Kulkarni, R.V. (2022). Augmentation of Handwritten Devanagari Character Dataset Using DCGAN. In: Agrawal, S., Gupta, K.K., Chan, J.H., Agrawal, J., Gupta, M. (eds) Machine Intelligence and Smart Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-9650-3_3
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DOI: https://doi.org/10.1007/978-981-16-9650-3_3
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