A Comparative Study of Image Processing Methods for Handwritten Digit Classification

Abstract

Handwritten digit recognition is a pivotal application of image processing and machine learning, with significant implications in areas such as postal services, banking, and document analysis. This paper delves into the methodologies used for recognizing handwritten digits, highlighting the role of image preprocessing, feature extraction, and classification algorithms. A comprehensive review of existing literature is provided, focusing on traditional techniques and advancements using deep learning models like Convolutional Neural Networks (CNNs). The paper emphasizes the importance of dataset quality, preprocessing methods, and robust models for achieving high accuracy. Future directions and challenges in handwritten digit recognition are also discussed.

DOI - https://doi.org/10.65525/SVUP.9788199565463.2026.87-102