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2023 | OriginalPaper | Chapter

Comparative Study of Machine Learning and Deep Learning Classifiers on Handwritten Numeral Recognition

Authors : Meenal Jabde, Chandrashekhar Patil, Shankar Mali, Amol Vibhute

Published in: International Symposium on Intelligent Informatics

Publisher: Springer Nature Singapore

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Abstract

Handwriting digit recognition is a computer technology that allows it to accept and decipher sound transcribed input from various sources, including paper reports, contact screens, and pictures. This paper presents the machine and Deep Learning approach for handwritten digit recognition from image input. This approach uses bench marked dataset MNIST English handwritten numeral digit dataset of size 70,000. Four machine learning and deep learning algorithms are explored, and a pattern recognition approach is used. We have explored the pattern matching approach and achieved 86% accuracy for the Decision Tree classifier, 91% accuracy for the Support Vector Machine classifier, 97% for Artificial Neural Network and 98.84% for Convolutional Neural Network. In the Deep Learning approach, the Convolutional Neural Network algorithm with the Vgg16 network is implemented to train the MNIST digit dataset and achieved an accuracy of 98.84%.

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Metadata
Title
Comparative Study of Machine Learning and Deep Learning Classifiers on Handwritten Numeral Recognition
Authors
Meenal Jabde
Chandrashekhar Patil
Shankar Mali
Amol Vibhute
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
International Symposium on Intelligent Informatics

Print ISBN: 978-981-19-8093-0

Electronic ISBN: 978-981-19-8094-7

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-19-8094-7_10