Digit Recognition Using MNIST Dataset

Abstract

Abstract: In this paper, we have performed handwritten digit recognition using MNIST datasets using Support Vector Machines (SVM), Multi-Layer Perceptron (MLP) and Convolution Neural Network (CNN) models. Our main goal is to compare the accuracy of the above models along with their execution time to obtain the best possible model for digit recognition. Reliability of humans over machines has never been so high that from classifying objects in photographs to adding sound to silent movies can all be done using deep learning and machine learning algorithms. Similarly, handwriting recognition is one of the important areas research and development with a range of possibilities that could be achieved. Handwriting recognition (HWR), also known as handwritten text recognition (HTR), is a capability computers to receive and interpret comprehensible handwritten input from sources such as paper documents, photos, touch screen