Analysis of machine learning algorithms for character recognition: a case study on handwritten digit recognition
This paper covers the work done in handwritten digit recognition and the various classifiers that have been developed. Methods like MLP, SVM, Bayesian networks, and random forests were discussed with their accuracy and are empirically evaluated. Boosted LetNet 4, an ensemble of various classifiers, has shown maximum efficiency among these methods.
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Hivatkozás
MLA: Khanday, Owais Mujtaba, and Samad Dadvandipour. "Analysis of machine learning algorithms for character recognition: a case study on handwritten digit recognition." Indonesian J. Elect. Eng. Comput. Sci 21.1 (2021): 574-581.
APA: Khanday, O. M., & Dadvandipour, S. (2021). Analysis of machine learning algorithms for character recognition: a case study on handwritten digit recognition. Indonesian J. Elect. Eng. Comput. Sci, 21(1), 574-581.
ISO690: KHANDAY, Owais Mujtaba; DADVANDIPOUR, Samad. Analysis of machine learning algorithms for character recognition: a case study on handwritten digit recognition. Indonesian J. Elect. Eng. Comput. Sci, 2021, 21.1: 574-581.
BibTeX:
@article{khanday2021analysis,
title={Analysis of machine learning algorithms for character recognition: a case study on handwritten digit recognition},
author={Khanday, Owais Mujtaba and Dadvandipour, Samad},
journal={Indonesian J. Elect. Eng. Comput. Sci},
volume={21},
number={1},
pages={574--581},
year={2021}
}