Design and Implementation of Handwritten Digit Recognition Using Deep Convolution Neural Networks

  • Ms. Mona Mulchandani, Mr. Chandrashekher S. Gode, Ms. Sajiya Sheikh
Keywords: Handwritten digit recognition, Convolutional Neural Network (CNN), Deep learning, MNIST dataset

Abstract

MNIST dataset is a huge collection of handwritten digit images along with proper annotations and labeling. With the dramatic advent of deep learning and underlying models, recognition of handwritten digits is heavily targeted in the literature. Convolutional neural network (CNN) is a most successful model in the field of computer vision. Due to its automatic feature extraction capability, CNNs are widely adopted for these kinds of recognition and detection tasks. In this work, we employ CNN for the handwritten digit recognition task using MNIST dataset and our proposed architectures achieves a remarkable accuracy. Moreover, we also present the future research direction in this handwritten digit recognition task.

Published
2021-07-07
How to Cite
Ms. Sajiya Sheikh, M. M. M. M. C. S. G. (2021). Design and Implementation of Handwritten Digit Recognition Using Deep Convolution Neural Networks. Design Engineering, 1093-1100. Retrieved from http://www.thedesignengineering.com/index.php/DE/article/view/2549
Section
Articles