Improved Recognition of Hand-Written Digits Using Convolutional Neural Network (CNN)

Authors

  • Huma Hafeez a:1:{s:5:"en_US";s:19:"Shandong University";}
  • Ch Asad Abbas Shandong University
  • Muhammad Usman Khan

Keywords:

Natural Language Processing, Hand-Written Digit Recognition, Convolutional Neural Network, Adam Optimizer, SGDM Optimizer

Abstract

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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Published

2021-12-31

How to Cite

Hafeez, H., Abbas, C. A., & Khan, M. U. (2021). Improved Recognition of Hand-Written Digits Using Convolutional Neural Network (CNN). University of Wah Journal of Science and Technology (UWJST), 5, 28–33. Retrieved from https://uwjst.org.pk/index.php/uwjst/article/view/69