Handwritten Digit Recognition is a technology that enables computers to identify and interpret digits written by hand. This process typically involves using machine learning algorithms to analyze images of handwritten numbers, converting them into a digital format that a computer can understand. The most common dataset used for training these models is the MNIST dataset, which contains thousands of labeled images of handwritten digits. The recognition process usually involves several steps, including image preprocessing, feature extraction, and classification. During classification, algorithms like Convolutional Neural Networks (CNNs) are often employed to improve accuracy. This technology is widely used in applications such as automated check processing and postal code recognition.