Comparative Analysis of Handwritten Digit Recognition Using Logistic Regression, SVM, KNN and CNN Algorithms
TL;DR
This paper is about recognizing handwritten digits from 0 to 9 from the well-known Modified National Institute of Standards and Technology dataset, then comparison takes place between machine learning algorithms like Support Vector Machine (SVM), logistic regression, K-Nearest Neighbor (KNN) and deep learning algorithm like CNN.
Abstract
The style of handwriting varies from person to person. Handwritten numbers are not always the same size, orientation and width. To develop a system to understand this, the machine recognizes handwritten digit images and classifies them into 10 digits (from 0 to 9).Handwritten digit recognition is a technology which is used for automatic recognizing and detecting handwritten digital data through various machine learning models. This paper uses a different machine learning algorithms to improve productivity and a variety of models to reduce complexity. Machine Learning is an artificial intelligence application which learns from previous experiences and it automatically improves with the previous experiences. This paper is about recognizing handwritten digits from 0 to 9 from the well-known Modified National Institute of Standards and Technology(MNIST) dataset, then comparison takes place between machine learning algorithms like Support Vector Machine(SVM), logistic regression, K-Nearest Neighbor (KNN) and deep learning algorithm like CNN