Handwritten Digit Recognition is the process of classifying handwritten digits into their respective numerical values. It is used widely in image processing and machine learning as an example of pattern recognition. The mathematical foundations of Handwritten Digit Recognition involve several procedures, including image processing, feature extraction, and classification. Pre-processing techniques include noise reduction, contrast enhancement, and image normalization. The Modified National Institute of Standards and Technology (MNIST) Dataset, which consists of a collection of 70,000+ images of handwritten digits labeled with their corresponding numerical values, is widely used as a benchmark dataset in Handwritten Digit Recognition. The accuracy of the Handwritten Digit Recognition model is measured using the ratio of correctly classified instances to the total samples in the testing set, with a score of 97% indicating a good level of accuracy.
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