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Handwritten Digit Recognition using Machine and Deep Learning Algorithms
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 176 - Number 42 |
| Year of Publication: 2020 |
| Authors: Ritik Dixit, Rishika Kushwah, Samay Pashine |
10.5120/ijca2020920550
|
Ritik Dixit, Rishika Kushwah, Samay Pashine . Handwritten Digit Recognition using Machine and Deep Learning Algorithms. International Journal of Computer Applications. 176, 42 ( Jul 2020), 27-33. DOI=10.5120/ijca2020920550
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, this paper illustrates handwritten digit recognition with the help of MNIST datasets using Support Vector Machines (SVM), Multi-Layer Perceptron (MLP), and Convolution Neural Network (CNN) models. The main objective of this paper is to compare the accuracy of the models stated above along with their execution time to get the best possible model for digit recognition.
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