Comparison on Logistic Regression, Random Forest, and CNN for Handwritten Digit Recognition

Abstract

The technology of handwritten digit and character recognition is the process of identifying handwritten numbers using computers or other devices, machine train itself to recognize the handwritten digits or characters from various sources like bank cheque, mails, images, etc. This paper is about CNN, Logistic Regression, and Random Forest algorithm in handwritten digit or character recognition system, the system works on MNIST dataset for training and testing the models, to get the best accuracy this work rewrites CNN, Logistic Regression, and Random Forest with python libraries. Finally, these algorithms are analyzed by comparing the accuracy and recognition duration. Where CNN got better accuracy than other algorithms