The MNIST Dataset is a large collection of handwritten digits commonly used for training various image processing systems. It consists of 70,000 grayscale images of digits from 0 to 9, each sized at 28x28 pixels. The dataset is divided into 60,000 training images and 10,000 testing images, making it a standard benchmark for evaluating machine learning algorithms. Developed by Yann LeCun and his colleagues in the 1990s, the MNIST Dataset has become a foundational resource in the field of computer vision and deep learning. Its simplicity and accessibility allow researchers and developers to test and compare different models effectively.