Handwritten Digit Classification Using Support Vector Machine Algorithm

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
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