Comparative Analysis of Machine Learning and Deep Learning Models for Handwritten Digit Recognition

Abstract

Handwritten digit recognition (HDR) forms a key component of computer vision systems, especially in optical character recognition (OCR). This study presents a comparative analysis of Machine Learning (ML) algorithms and Deep Learning (DL) models for HDR tasks. A contour-based segmentation technique was applied in preprocessing to enhance feature extraction by detecting digit boundaries and reducing noise. ML models, including K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), and DL architectures, such as Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs), were evaluated on the Modified National Institute of Standards and Technology (MNIST) and the National Institute of Standards and Technology (NIST) datasets. The results demonstrate that DL models significantly outperform ML algorithms in terms of accuracy and robustness, while the KNN model achieved acceptable results. The results underline the importance of contour-based preprocessing in boosting deep learning techniques for HDR.