Comparisons of Deep Learning Algorithms for MNIST in Real-Time Environment

Abstract

Recognizing handwritten digits was challenging task in a couple of years ago. Thanks to machine learning algorithms, today, the issue has solved but those algorithms require much time to train and to recognize digits. Thus, using one of those algorithms to an application that works in real-time, is complex. Notwithstanding use of a trained model, if the model uses deep neural networks it requires much more time to make a prediction and becomes more complicated as well as memory usage also increases. It leads real-time application to delay and to work slowly even using trained model. A memory usage is also essential as using smaller memory of trained models works considerable faster comparing to models with huge pre-processed memory. For this work, we implemented four models on the basis of unlike algorithms which are capsule network, deep residual learning model, convolutional neural network and multinomial logistic regression to recognize handwritten digits. These models have unlike structure and they have showed a great results on MNIST before so we aim to compare them in real-time environment. The dataset MNIST seems most suitable for this work since it is popular in the field and basically used in many state-of-the-art algorithms beyond those models mentioned above. We purpose revealing most suitable algorithm to recognize handwritten digits in real-time environment. Also, we give comparisons of train and evaluation time, memory usage and other essential indexes of all four models.