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Traditional handwriting recognition systems depend on various features and prior knowledge making its practical implementation more challenging. Handwriting recognition system has focused deep learning algorithms in recent times and achieved good performance accuracy but still, needs to be improved in terms of recognition accuracy due to rapid growth of data and massive computations. Deep learning is remarkably being employed in various fields because of diverse range of applications. In this paper, we have employed CNN (Convolutional Neural Network) with two different optimizers and five different epoch number to observe the variations of accuracy for classifying different handwritten digits. We have trained seven-layered CNN with Adam and SGDM optimizer on MNIST (Modified National Institute of Standards and Technology) dataset and found that for maximum number of iterations, Adam optimizer outperform the SGDM optimizer in terms of accuracy which is found to be 99.50%, respectively.
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