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Handwritten Digit Recognition is probably one of the most exciting works in the field of science and technology as it is a hard task for the machines to recognize the digits which are written by different people. The handwritten digits may not be perfect and also consist of different flavors. And there is a necessity for handwritten digit recognition in many real-time purposes. The widely used MNIST dataset consists of almost 60000 handwritten digits. And to classify these kinds of images, many machine learning algorithms are used. This paper presents an in-depth analysis of accuracies and performances of Support Vector Machines (SVM), Neural Networks (NN), Decision Tree (DT) algorithms using Microsoft Azure ML Studio.
Goutham Cheedella. 2020. "Critique of Various Algorithms for Handwritten Digit Recognition Using Azure ML Studio". Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 20 (GJCST Volume 20 Issue D1).
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
v1.2
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Total Score: 101
Country: India
Subject: Global Journal of Computer Science and Technology - D: Neural & AI
Authors: Goutham Cheedella (PhD/Dr. count: 0)
View Count (all-time): 384
Total Views (Real + Logic): 1484
Total Downloads (simulated): 79
Publish Date: 2020 01, Wed
Monthly Totals (Real + Logic):
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