Work

Handwritten Digit Recognition using Machine Learning Classifiers

Year: 2025

Type: article

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.

Source: Journal of International Crisis and Risk Communication Research

Author: Ahiduzzaman, Md

Language: English

Topic: Handwritten Text Recognition Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7111435446

FWCI:

Cites:

Cited by:

Related to: 10