Dr SK Mahboob Basha, Sri Harsha Dixit, SV.Swathi, N.Shashank, K.Prashanth, 02 06 Handwritten digit classification is a fundamental problem in the field of machine learning and computer vision It involves recognizing handwritten digits 09 from images The MNIST dataset consisting of 28x28 pixel grayscale images of handwritten digits is a widely used benchmark for this task In this we embark on the MNIST Handwritten Digit Classification Challenge aiming to delve into image data analysis and explore different machine learning algorithms for accurate classification Traditionally handwritten digit classification was done manually by human experts With the advent of computer vision and machine learning automated systems have been developed to tackle this task However traditional systems often relied heavily on handcrafted features and simplistic algorithms which limited their accuracy and scalability Moreover these systems struggled with variations in handwriting styles noise in images and computational inefficiency The problem we address is to build an accurate and efficient system for classifying handwritten digits using machine learning techniques Accurate handwritten digit classification has numerous realworld applications including postal automation bank check processing and digit recognition in forms By developing robust classification models we can enhance the efficiency and reliability of such applications leading to improved productivity and cost savings The proposed system employs modern machine learning techniques including Generalized Learning Vector Quantization GLVQ and Support Vector Machine SVM to classify handwritten digits effectively These algorithms offer advantages such as automatic feature extraction robustness to variations and scalability to large datasets Additionally we leverage data visualization and performance evaluation techniques to gain insights into the classification process and ensure model reliability Overall our system aims to provide an accessible and insightful journey into image data analysis for beginners in the field Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026