Abstract
Kannada is a Dravidian Language spoken by over 80 million people all over the world; Kannada script’s character recognition system has wide range applications in education, healthcare, finance and many more sectors. Building it involves many phases and makes it more challenging because of the nature of script including horizontal skews and curves. Although challenging, several attempts have been made in the past to develop methods to either avoid the intermediate steps or reduce the complexity involved, thereby effecting the time and cost factors. The objective of this work is to understand Why, achieving effective recognition rates using recent technology is still challenging? And Is it possible to overcome the existing challenges and drawbacks by retraining different deep neural networks, using different Transfer Learning techniques? Four different Transfer Learning techniques, two different pre-trained deep neural networks and different dataset were employed, resulting in an exceptional recognition rate of 92.5%.
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Reddy, P.R., Mamatha, H.R. (2021). Isolated Kannada Character Recognition Using Transfer Learning. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_125
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