  The reliance of humans over machines has never been so high such that from
object classification in photographs to adding sound to silent movies
everything can be performed with the help of deep learning and machine learning
algorithms. Likewise, Handwritten text recognition is one of the significant
areas of research and development with a streaming number of possibilities that
could be attained. Handwriting recognition (HWR), also known as Handwritten
Text Recognition (HTR), is the ability of a computer to receive and interpret
intelligible handwritten input from sources such as paper documents,
photographs, touch-screens and other devices [1]. Apparently, in this paper, we
have performed handwritten digit recognition with the help of MNIST datasets
using Support Vector Machines (SVM), Multi-Layer Perceptron (MLP) and
Convolution Neural Network (CNN) models. Our main objective is to compare the
accuracy of the models stated above along with their execution time to get the
best possible model for digit recognition.
