Machine Learning Techniques for Handwritten Digit Recognition

Ahmad Taher Azar; Alaa Khamis; Nashwa Ahmad Kamal; Brian Galli

doi:10.1007/978-3-030-44289-7_39

Machine Learning Techniques for Handwritten Digit Recognition

Ahmad Taher Azar^*
, Alaa Khamis
, Nashwa Ahmad Kamal
, Brian Galli

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

The following study examines how various classification algorithms perform on the problem of handwritten digit recognition. The classifiers discussed are k-Nearest Neighbour (k-NN), Single Classification Decision Trees and Bagged Decision Trees. These algorithms were evaluated with the use of information from the United States Postal Service (USPS). This study’s results show that the k-NN classifier had the fastest performance while the bagged decision trees were the slowest. In terms of classification performance, the bagged decision tree method was found to have the fewest misclassifications and outperformed k-NN and single classification trees in all of the considered metrics.

Original language	English
Title of host publication	Proceedings of the International Conference on Artificial Intelligence and Computer Visio, AICV 2020
Editors	Aboul-Ella Hassanien, Ahmad Taher Azar, Tarek Gaber, Diego Oliva, Fahmy M. Tolba
Publisher	Springer
Pages	414-426
Number of pages	13
ISBN (Print)	9783030442880
DOIs	https://doi.org/10.1007/978-3-030-44289-7_39
State	Published - 2020
Externally published	Yes
Event	1st International Conference on Artificial Intelligence and Computer Visions, AICV 2020 - Cairo, Egypt Duration: 8 Apr 2020 → 10 Apr 2020

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1153 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	1st International Conference on Artificial Intelligence and Computer Visions, AICV 2020
Country/Territory	Egypt
City	Cairo
Period	8/04/20 → 10/04/20

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

Bagged Decision Tree
Optical Character Recognition
Single Classification Decision Trees
k-Nearest Neighbor

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science

Access to Document

10.1007/978-3-030-44289-7_39

Cite this

Azar, A. T., Khamis, A., Kamal, N. A., & Galli, B. (2020). Machine Learning Techniques for Handwritten Digit Recognition. In A.-E. Hassanien, A. T. Azar, T. Gaber, D. Oliva, & F. M. Tolba (Eds.), Proceedings of the International Conference on Artificial Intelligence and Computer Visio, AICV 2020 (pp. 414-426). (Advances in Intelligent Systems and Computing; Vol. 1153 AISC). Springer. https://doi.org/10.1007/978-3-030-44289-7_39

Azar, Ahmad Taher ; Khamis, Alaa ; Kamal, Nashwa Ahmad et al. / Machine Learning Techniques for Handwritten Digit Recognition. Proceedings of the International Conference on Artificial Intelligence and Computer Visio, AICV 2020. editor / Aboul-Ella Hassanien ; Ahmad Taher Azar ; Tarek Gaber ; Diego Oliva ; Fahmy M. Tolba. Springer, 2020. pp. 414-426 (Advances in Intelligent Systems and Computing).

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title = "Machine Learning Techniques for Handwritten Digit Recognition",

abstract = "The following study examines how various classification algorithms perform on the problem of handwritten digit recognition. The classifiers discussed are k-Nearest Neighbour (k-NN), Single Classification Decision Trees and Bagged Decision Trees. These algorithms were evaluated with the use of information from the United States Postal Service (USPS). This study{\textquoteright}s results show that the k-NN classifier had the fastest performance while the bagged decision trees were the slowest. In terms of classification performance, the bagged decision tree method was found to have the fewest misclassifications and outperformed k-NN and single classification trees in all of the considered metrics.",

keywords = "Bagged Decision Tree, Optical Character Recognition, Single Classification Decision Trees, k-Nearest Neighbor",

author = "Azar, \{Ahmad Taher\} and Alaa Khamis and Kamal, \{Nashwa Ahmad\} and Brian Galli",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 1st International Conference on Artificial Intelligence and Computer Visions, AICV 2020 ; Conference date: 08-04-2020 Through 10-04-2020",

year = "2020",

doi = "10.1007/978-3-030-44289-7\_39",

language = "English",

isbn = "9783030442880",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer",

pages = "414--426",

editor = "Aboul-Ella Hassanien and Azar, \{Ahmad Taher\} and Tarek Gaber and Diego Oliva and Tolba, \{Fahmy M.\}",

booktitle = "Proceedings of the International Conference on Artificial Intelligence and Computer Visio, AICV 2020",

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Azar, AT, Khamis, A, Kamal, NA & Galli, B 2020, Machine Learning Techniques for Handwritten Digit Recognition. in A-E Hassanien, AT Azar, T Gaber, D Oliva & FM Tolba (eds), Proceedings of the International Conference on Artificial Intelligence and Computer Visio, AICV 2020. Advances in Intelligent Systems and Computing, vol. 1153 AISC, Springer, pp. 414-426, 1st International Conference on Artificial Intelligence and Computer Visions, AICV 2020, Cairo, Egypt, 8/04/20. https://doi.org/10.1007/978-3-030-44289-7_39

Machine Learning Techniques for Handwritten Digit Recognition. / Azar, Ahmad Taher; Khamis, Alaa; Kamal, Nashwa Ahmad et al.
Proceedings of the International Conference on Artificial Intelligence and Computer Visio, AICV 2020. ed. / Aboul-Ella Hassanien; Ahmad Taher Azar; Tarek Gaber; Diego Oliva; Fahmy M. Tolba. Springer, 2020. p. 414-426 (Advances in Intelligent Systems and Computing; Vol. 1153 AISC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Machine Learning Techniques for Handwritten Digit Recognition

AU - Azar, Ahmad Taher

AU - Khamis, Alaa

AU - Kamal, Nashwa Ahmad

AU - Galli, Brian

PY - 2020

Y1 - 2020

N2 - The following study examines how various classification algorithms perform on the problem of handwritten digit recognition. The classifiers discussed are k-Nearest Neighbour (k-NN), Single Classification Decision Trees and Bagged Decision Trees. These algorithms were evaluated with the use of information from the United States Postal Service (USPS). This study’s results show that the k-NN classifier had the fastest performance while the bagged decision trees were the slowest. In terms of classification performance, the bagged decision tree method was found to have the fewest misclassifications and outperformed k-NN and single classification trees in all of the considered metrics.

AB - The following study examines how various classification algorithms perform on the problem of handwritten digit recognition. The classifiers discussed are k-Nearest Neighbour (k-NN), Single Classification Decision Trees and Bagged Decision Trees. These algorithms were evaluated with the use of information from the United States Postal Service (USPS). This study’s results show that the k-NN classifier had the fastest performance while the bagged decision trees were the slowest. In terms of classification performance, the bagged decision tree method was found to have the fewest misclassifications and outperformed k-NN and single classification trees in all of the considered metrics.

KW - Bagged Decision Tree

KW - Optical Character Recognition

KW - Single Classification Decision Trees

KW - k-Nearest Neighbor

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DO - 10.1007/978-3-030-44289-7_39

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SN - 9783030442880

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BT - Proceedings of the International Conference on Artificial Intelligence and Computer Visio, AICV 2020

A2 - Hassanien, Aboul-Ella

A2 - Azar, Ahmad Taher

A2 - Gaber, Tarek

A2 - Oliva, Diego

A2 - Tolba, Fahmy M.

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Azar AT, Khamis A, Kamal NA, Galli B. Machine Learning Techniques for Handwritten Digit Recognition. In Hassanien AE, Azar AT, Gaber T, Oliva D, Tolba FM, editors, Proceedings of the International Conference on Artificial Intelligence and Computer Visio, AICV 2020. Springer. 2020. p. 414-426. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-44289-7_39

Machine Learning Techniques for Handwritten Digit Recognition

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this