COMPREHENSIVE STUDY OF MACHINE LEARNING TECHNIQUES AND NEURAL NETWORKS FOR HANDWRITTEN DIGIT RECOGNITION: METHODS, ALGORITHMS AND DATASETS

Handwritten digit recognition is a key problem in computer vision, addressed using both traditional machine learning algorithms and neural networks. Traditional methods like K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Random Forests rely on feature extraction techniques to classify digit images. In contrast, Convolutional Neural Networks (CNNs) automatically learn features from raw pixel data, offering superior accuracy, especially on complex datasets like MNIST. Datasets such as MNIST, EMNIST, and SVHN provide the necessary labeled images for training these models. While traditional algorithms are simpler and more interpretable, CNNs excel in performance, making them the preferred choice for modern handwritten digit recognition tasks.