Handwritten Digit Recognition using Machine Learning Classifiers

Author(s)

Md Ahiduzzaman, University of Central FloridaFollow

Keywords

Handwritten digit recognition, Logistic Regression, k-Nearest Neighbors, Convolutional Neural Networks, MNIST dataset, machine learning

Abstract

This project explores and compares the performance of various machine learning classifiers for handwritten digit recognition using the MNIST dataset. The classifiers include Logistic Regression, k-Nearest Neighbors, and Convolutional Neural Networks. Each classifier is evaluated based on accuracy, precision, recall, F1-score, and confusion matrix analysis.

Semester

Fall 2025

Course Name

STA 6366 Data Science 1

Instructor Name

Dr. Rui Xie

College

College of Sciences

STARS Citation

Ahiduzzaman, Md, "Handwritten Digit Recognition using Machine Learning Classifiers" (2025). Data Science and Data Mining. 48.
https://stars.library.ucf.edu/data-science-mining/48

Accessibility Status

PDF accessibility verified using Adobe Acrobat Pro accessibility checker.