MNSIT Handwritten Digit Recognition Using Machine Learning Classification Algorithms

The paper discusses the use of machine learning in recognizing handwritten digits and text, which has wide applications in areas such as surveillance, healthcare, and document analysis. The study focuses on evaluating the accuracy and variability of classifying handwritten digits with different numbers of hidden layers using the Modified National Institute of Standards and Technology (MNIST) dataset, and compares the performance of common machine learning algorithms such as SVM, KNN, and RFC. The study notes that recognizing handwritten digits and text is challenging due to their dissimilarities in size, thickness, position, and orientation. The ability to accurately recognize handwritten digits is essential in various fields, including banking, post offices, and tax files. The paper demonstrates handwritten digit recognition (HDR) using the MNIST dataset and selected classification algorithms. Overall, handwriting recognition is a major area of development with many possibilities for applications.