Open access

Comparative Evaluation of Machine Learning Techniques for Handwritten Digit Classification Using the MNIST Dataset

Jun 2026 · AlKadhim Journal for Computer Science · Vol 2, pp. 58 · 1 citation · 4 references

TL;DR

A comparative analysis of a number of machine learning and deep learning algorithms and models, namely Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, SVM and Convolutional Neural Networks using the MNIST dataset proves that conventional machine learning models, especially SVM and Random Forest, perform competitively.

Abstract

Machine learning (ML) and deep learning (DL) have advanced at a very rapid pace; thus, improving image classification in many fields. MNIST data is one of the most popular benchmark datasets to compare classification algorithms and it is represented by 70,000 grayscale images of handwritten digits (09). This paper will make a comparative analysis of a number of machine learning and deep learning algorithms and models, namely Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM) and Convolutional Neural Networks (CNN) using the MNIST dataset. All of the models were trained and tested using the same preprocessing methods, including normalization and feature scaling. Several evaluation metrics were applied to evaluate the performance, and they included accuracy, precision, recall, F1-score, and confusion matrix. The findings prove that conventional machine learning models, especially SVM and Random Forest, perform competitively. Nonetheless, CNN is more effective as compared to classica

DOI Semantic Scholar

Read PDF

Similar papers

Open access Aug 2026

Performance Comparison of Support Vector Machine and Decision Trees for the Classification of Handwritten Digits

Evaluated and compares the performance of Support Vector Machine and Decision Tree classifiers for handwritten digit recognition using the Modified National Institute of Standards and Technology (MNIST) dataset and indicates that SVM provides superior classification performance for handwritten digit recognition, while...

Bilikisu Temilade Azeez, Stephen Olatunde Olabiyisi, Modupe O. Alade et al. · International Journal of Lat... · 0 citations

Open access Jul 2026

Handwritten Digit Recognition Using Deep Learning

The Handwritten Digit Recognition System is a machine learning and deep learning–based project developed to accurately identify handwritten numerical digits from input images by using image processing techniques and a Convolutional Neural Network model trained on the MNIST dataset.

R. Rajesh, P. Sravani · International Journal for Re... · 0 citations

Review Open access Jul 2026

A Comparative Study of Deep Learning Models for Image Classification: Simple MLP, Deep MLP, Basic CNN, LeNet CNN

This study presents a comparative analysis of four deep learning architectures: Simple Multi-Layer Perceptron (MLP), Deep MLP, Basic Convolutional Neural Network (CNN), and LeNet Convolutional Neural Network (CNN). The models are evaluated on the MNIST handwritten digit dataset under identical experimental conditions u...

Baffoe Nicholas, Jephthah Kwame Lanor · Andalasian International Jou... · 0 citations

Handwritten Digit Recognition Based on Convolutional Neural Network and Its Application in Overlapping Image Processing

A serial cascade of lightweight CNN and spectrum normalized GAN and spectrum normalized GAN, integrating CBAM attention mechanism is proposed, integrating CBAM attention mechanism, with good experimental results.

Han Wang · 0 citations

Open access Aug 2026

Automatic Classification of Clean and Noisy Offline Handwritten Signatures Using Hybrid Machine Learning

A new approach is presented here that integrates traditional handcrafted texture features such as Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) and Gray-Level Co-occurrence Matrix (GLCM) with deep learning models with deep learning models.

B. L. Thejashwini, H. S. Nagendraswamy, Rajashekara M et al. · International journal of com... · 0 citations

Conference Jul 2026

Comprehensive Analysis of Face Recognition using Machine Learning and Deep Learning Models

Face recognition is a cornerstone in computer vision with its applications covering emotion analysis, healthcare and human computer interaction. This paper gives a complete explanation and classification of face recognition models by categorizing into Machine learning (ML), Deep Learning (DL) and hybrid techniques. Tra...

C. Prathibha, Ashwini S. Shivannavar · 2026 5th International Confe... · 0 citations