Abstract
Handwritten Greek character recognition is considered as one of the ways for genuine character recognition. In this paper, convolutional neural networks (CNNs) is applied on disconnected transcribed Greek character to identify and recognize properly. Inception V3 CNN model, with unique settings of the quantity of neurons in each layer is utilized and the interfacing route between certain layers. Yields of the CNN are set with modified adjusting codes, wherein CNN has the capacity to discard recognition along these lines. For preparing of the CNN, a mistake tests-based fortification learning methodology is created. Results show that the proposed system tend to achieve an accuracy of 99% and is far better when compared to the existing techniques.
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Tallapragada, V.V.S., Alivelu Manga, N., Nagabhushanam, M.V., Venkatanaresh, M. (2022). Greek Handwritten Character Recognition Using Inception V3. In: Somani, A.K., Mundra, A., Doss, R., Bhattacharya, S. (eds) Smart Systems: Innovations in Computing. Smart Innovation, Systems and Technologies, vol 235. Springer, Singapore. https://doi.org/10.1007/978-981-16-2877-1_23
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DOI: https://doi.org/10.1007/978-981-16-2877-1_23
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