Recognition of handwritten characters using deep convolutional neural network

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Abstract

Handwritten character recognition (HCR) mainly entails optical character recognition. However, HCR involves in formatting and segmentation of the input. HCR is still an active area of research due to the fact that numerous verification in writing style, shape, size to individuals. The main difficult part of Indian handwritten recognition has overlapping between characters. These overlapping shaped characters are difficult to recognize that may lead to low recognition rate. These factors also increase the complexity of handwritten character recognition. This paper proposes a new approach to identify handwritten characters for Telugu language using Deep Learning (DL). The proposed work can be enhance the recognition rate of individual characters. The proposed approach recognizes with overall accuracy is 94%.

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Jagan Mohan Reddy, D., & Vishnuvardhan Reddy, A. (2019). Recognition of handwritten characters using deep convolutional neural network. International Journal of Innovative Technology and Exploring Engineering, 8(6 Special Issue 4), 314–317. https://doi.org/10.35940/ijitee.F1064.0486S419

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