Impact of Machine Learning and Deep Learning Models on Handwritten Digits and Letters Recognition (HDaLR)

Impact of Machine Learning and Deep Learning Models on Handwritten Digits and Letters Recognition (HDaLR)
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© 2024 by IJETT Journal
Volume-72 Issue-1
Year of Publication : 2024
Author : Ratna Nitin Patil, Yogita Deepak Sinkar, Shitalkumar Adhar Rawandale, Varsha D. Jadhav
DOI : 10.14445/22315381/IJETT-V72I1P105

How to Cite?

Ratna Nitin Patil, Yogita Deepak Sinkar, Shitalkumar Adhar Rawandale, Varsha D. Jadhav, "Impact of Machine Learning and Deep Learning Models on Handwritten Digits and Letters Recognition (HDaLR)," International Journal of Engineering Trends and Technology, vol. 72, no. 1, pp. 48-55, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I1P105

Abstract
In numerous practical applications, such as data form entry, postal code sorting, and bank check account processing, handwritten digit recognition is one of the crucial and difficult tasks. Because each person writes in a distinct way with varying sizes, widths, and slopes, it can be challenging to recognise digits. Various artificial neural network-based models have been used in the past for pattern matching. While conducting the experiment, significant differences in the use of fonts by various authors were observed using the MNIST (Modified National Institute of Standards and Technology database) dataset as a benchmark. In this study, we evaluated machine learning algorithms on the MNIST dataset, including Naive Bayes, K-Nearest Neighbor, Support Vector Machine, Decision Tree, Random Forest, Artificial Neural Network, Convolution Neural Network, and Long Short-Term Memory. The purpose of this research is to evaluate and contrast the effectiveness of deep learning and machine learning models over handwritten letters and digits datasets. It was noted that CNN has outperformed, and the accuracy obtained is 99.9% over the MNIST dataset and 88% over the EMNIST dataset. Every identification approach faces the crucial challenge of extracting key features, and deep learning has been used to solve this problem with results that have been evaluated.

Keywords
Handwritten digits, MNIST, SVM, Deep learning, CNN, LSTM.

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