Handwritten Digit Recognition using Machine and Deep Learning Algorithms

TL;DR

This paper compares machine learning and deep learning models, including SVM, MLP, and CNN, for handwritten digit recognition using the MNIST dataset, focusing on accuracy and execution time to identify the most effective approach.

Contribution

It provides a comparative analysis of traditional and deep learning models for handwritten digit recognition, highlighting their performance differences.

Findings

CNN achieved the highest accuracy among models.

Support Vector Machines had the fastest execution time.

Deep learning models outperformed traditional machine learning in accuracy.

Abstract

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.