This paper is published in Volume-7, Issue-6, 2021
Area
CNN
Author
V. Serisha, Beeraka Sridevi, Shriya Singh, Dr. B. Karthik
Org/Univ
Bharath University, Chennai, Tamil Nadu, India
Pub. Date
23 November, 2021
Paper ID
V7I6-1212
Publisher
Keywords
Convolutional Neural Network, Handwritten Digit Classification, MNIST Datasets, Artificial Neural Network, Deep Learning

Citationsacebook

IEEE
V. Serisha, Beeraka Sridevi, Shriya Singh, Dr. B. Karthik. Handwritten Digit Classification using CNN, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
V. Serisha, Beeraka Sridevi, Shriya Singh, Dr. B. Karthik (2021). Handwritten Digit Classification using CNN. International Journal of Advance Research, Ideas and Innovations in Technology, 7(6) www.IJARIIT.com.

MLA
V. Serisha, Beeraka Sridevi, Shriya Singh, Dr. B. Karthik. "Handwritten Digit Classification using CNN." International Journal of Advance Research, Ideas and Innovations in Technology 7.6 (2021). www.IJARIIT.com.

Abstract

Since people's writing styles vary in form and orientation, handwritten digit identification is a difficult job. Two steps are essential for the production of effective handwritten digit recognition. The extraction of discriminating features from handwritten images is the first step, and the classification of new digit images is the second. Convolutional Neural Networks (CNN) are at the heart of spectacular developments in deep learning that combine Artificial Neural Networks (ANN) and cutting-edge deep learning techniques. Pattern recognition, sentence classification, speech recognition, face recognition, text categorization, document interpretation, scene recognition, and handwritten digit recognition are only a few of the applications. Our project's aim is to improve detection accuracy by expanding the model depth. This is accomplished by increasing the number of layers and the number of filters. The Modified National Institute of Standards and Technology (MNIST) datasets were used for this performance assessment using CNN.
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