This project focuses on building a model to recognize handwritten digits (0–9) from images, using the popular MNIST dataset. It involves training a classification algorithm, often a convolutional neural network (CNN), to learn from thousands of labeled digit images. The model can then accurately predict the digit in new, unseen images.