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282076

MLHandwrittenRecognition: Handwritten Digit Recognition using Machine Learning Algorithms

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Last updated: 24 Dec 2024

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Abstract

Handwritten digit recognition has remained a topic of interest to computer vision scientists. Its origination precedes the emergence of the machine as it is a crucial component of the digital transformation of the majority of institutions in numerous fields. With the uprising of machine models, choosing a satisfactory and fit algorithm for this multi-class (0-9) classification problem became challenging. This paper aims to compare seven machine learning algorithms in terms of their performance metrics in recognizing handwritten digits employing two datasets. The - Nearest Neighbors (kNN), Support Vector Machine (SVM), Logistic Regression, Neural Network, Random Forest (RF), Naive Bayes, and Decision Tree models are accordingly evaluated concerning the Area Under the Curve (AUC), accuracy (ACC), F1-score (F1), precision (PREC), and recall (REC). The widely used Modified National Institute of Standards and Technology database (MNIST) dataset and the Handwritten Digit Classification dataset (HDC) have been the providers of the images on which this research is conducted. The results confirm that the Neural Networks model is a great classifier for this problem; however, it presents similar results to other machine learning classifiers in several cases. Therefore, this paper does not provide an absolute choice of a classifier for the handwritten digit recognition problem but rather explains the reason behind the performance of each model.

DOI

10.21608/jocc.2023.282076

Keywords

OCR, Handwritten digit, Machine Learning, Computer Vision

Authors

First Name

Diaa

Last Name

AbdElminaam

MiddleName

s

Affiliation

Department of Data Science , Faculty of Computer Science , Misr International University , Cairo , Egypt

Email

diaa.salama@miuegypt.edu.eg

City

-

Orcid

0000-0002-1544-9906

First Name

Farah

Last Name

Essam

MiddleName

-

Affiliation

Faculty of Computer Science Misr International University, Cairo, Egypt

Email

farah1900452@miuegypt.edu.eg

City

cairo

Orcid

-

First Name

Hanein

Last Name

Samy

MiddleName

-

Affiliation

Faculty of Computer Science Misr International University, Cairo, Egypt

Email

hanien1903163@miuegypt.edu.eg

City

cairo

Orcid

-

First Name

Judy

Last Name

Wagdy

MiddleName

-

Affiliation

Faculty of Computer Science Misr International University, Cairo, Egypt

Email

judy1902181@miuegypt.edu.eg

City

cairo

Orcid

-

First Name

steven

Last Name

Albert

MiddleName

-

Affiliation

Faculty of Computer Science Misr International University, Cairo, Egypt

Email

steven1901611@miuegypt.edu.eg

City

cairo

Orcid

-

Volume

2

Article Issue

1

Related Issue

39171

Issue Date

2023-01-01

Receive Date

2022-10-16

Publish Date

2023-01-25

Page Start

9

Page End

19

Online ISSN

2636-3577

Article File

JOCC_Volume 2_Issue 1_Pages 9-19.pdf

PDF . 778.9kB

Link

https://jocc.journals.ekb.eg/article_282076.html

Detail API

https://jocc.journals.ekb.eg/service?article_code=282076

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2

Type

Original Article

Type Code

731

Publication Type

Journal

Publication Title

Journal of Computing and Communication

Publication Link

https://jocc.journals.ekb.eg/

MainTitle

MLHandwrittenRecognition: Handwritten Digit Recognition using Machine Learning Algorithms

Details

Type

Article

Created At

24 Dec 2024