Critique of Various Algorithms for Handwritten Digit Recognition Using Azure ML Studio

Critique of Various Algorithms for Handwritten Digit Recognition Using Azure ML Studio

Goutham Cheedella

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Jawaharlal Nehru Technological University, Kakinada

Critique of Various Algorithms for Handwritten Digit Recognition Using Azure ML Studio

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Abstract

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.

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1 Cites in Article

Reference Format

Alexander Seewald (2011). On the Brittleness of Handwritten Digit Recognition Models.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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Not applicable for this article.

How to Cite This Article

Goutham Cheedella. 2020. \u201cCritique of Various Algorithms for Handwritten Digit Recognition Using Azure ML Studio\u201d. Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 20 (GJCST Volume 20 Issue D1).

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GJCST Volume 20 Issue D1

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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GJCST-D Classification I.2.2

Submission ReceivedDecember 11, 2019
Peer Review Double Blind
Handling Editor
Accepted December 31, 2019
Published January 15, 2020

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v1.2

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June 8, 2020

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