This document summarizes a research paper that compares different machine learning models for classifying handwritten digits using the MNIST dataset. It finds that a Support Vector Machine Classifier achieves the highest accuracy of 98.3% compared to 96.3% for a Random Forest Classifier and 88.97% for a Logistic Regression model. The paper preprocesses the images, implements the three classifiers, and evaluates their performance based on accuracy and other metrics like precision and recall. It concludes that the SVM Classifier is the most accurate for classifying handwritten digits from the MNIST dataset.