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A CNN based handwritten numeral recognition model for four arithmetic operations

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General Information
Title
A CNN based handwritten numeral recognition model for four arithmetic operations
Type
Article
Main Author
Wang, Shir Li
Additional Authors
  • Wei, Chen Shan
  • Foo, Ng Theam
  • Dzati Athiard Ramli
Subject
L Education
ISSN
1877-0509
Language Code
eng
Bibliographic Details
Place of Production
Tanjung Malim
Publisher
Fakulti Seni, Komputeran dan Industri Kreatif
Year of publication
2021
Notes
Procedia Computer Science
Degree Type
Others
Corporate Name
Universiti Pendidikan Sultan Idris
HTTP Link
Access Web Resource

Abstract

The pandemic of Covid-19 has caused a shift of paradigm of education, from face-to-face to e-learning. E-learning leads to an escalation in digitalization of handwritten documents because it requires submission of homework and assignments through online. To help teachers in checking digitalized handwritten homework, this paper proposes an automatic checking system based on a convolutional neural network (CNN) for handwritten numeral recognition. The CNN is used to recognize four arithmetic operations in mathematical questions consisting of addition, deduction, multiplication and division. The performance CNN in handwritten numeral recognition have been optimized in terms of activation function and gradient descent algorithm. The proposed CNN is also trained and tested with the MNIST handwritten data set. The experimental results show that the recognition accuracy the improved CNN improves to a certain extent as compared to before optimization. ? 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International.

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