Handwritten Digit Classification Using Support Vector Machine Algorithm
Author(s):
Vasundhara Rao, S.V.S.Harshitha, Dr.R.V.Ramana Chary
Keywords:
Handwritten Digits, Machine Learning, MNIST Dataset, Support Vector Machine.
Abstract
The process of converting handwritten digits into digital format can be challenging because of many variations that can occur due to variation in size and orientation. They differ from person to person. There is also a possibility where a person can write a single digit in various styles. To a human, it is a very difficult task to determine the digits. This is where we can use machine learning approach to solve this problem. Machine Learning is an invaluable tool when used to learn from large datasets, identify patterns and relationships and make decisions. One such machine learning algorithm is Support Vector Machine Algorithm. SVM is a classification algorithm which classifies data into categories based on patterns. In the proposed work, we developed a model using SVM which should correctly classify the handwritten digits from 0-9 based on the pixel values. We are using MNIST Dataset for classification.
Article Details
Unique Paper ID: 158674

Publication Volume & Issue: Volume 9, Issue 10

Page(s): 372 - 374
Article Preview & Download


Share This Article

Join our RMS

Conference Alert

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

Call For Paper

Volume 11 Issue 1

Last Date for paper submitting for Latest Issue is 25 June 2024

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews