Convolutional Neural Networks (CNNs) have been confirmed as a powerful technique for classification of visual inputs like handwritten digits and faces recognition. Hindi handwritten character recognition (HHCR) is one of the challenging issues in machine vision. This study aims to investigate the performance of Convolutional neural networks (CNNs) on HHCR problems. To investigate the performance of different CNNs, a dataset of Hindi handwritten characters has been used as ground truth data. Different optimizers have been implemented on different parameters to determine the test accuracy of the proposed architecture.

Convolutional neural network, Handwritten character recognition, Deep learning, Hindi character dataset

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