Akademik Veri Yönetim Sistemi
2nd International Graduate Studies Congress, Balıkesir, Türkiye, 8 - 11 Haziran 2022, ss.75-82
Recognizing handwritten digits in a computer vision system is a challenging problem
that is critical for a range of new applications. Optical character recognition is a science that
permits the analysis, editing, and search of numerous kinds of texts and pictures. Over the last
few decades, academics have used machine learning methods to automate the analysis of
handwritten and printed documents in order to convert them to electronic format.
Six distinct machine learning methods were used in this work to categorize handwritten digits
from the MNIST dataset: KNN, LDA, QDA, NB, SVM linear, and SVM polynomial. As a
consequence, the QDA algorithm achieved the highest average score of 94.91 percent, whereas
SVM linear had the lowest average score of 85.39 percent.