Handwritten Digit Recognition Using Support Vector Machine

Authors

  • Aditya Naik Dept. of Electronics Engineering, Vishwakarma Institute of Technology, Pune
  • Vijay Gaikwad Dept. of Electronics Engineering, Vishwakarma Institute of Technology, Pune

DOI:

https://doi.org/10.26438/ijcse/v8i7.162165

Keywords:

Computer Vision, Machine Learning, Classifier, SVM, Digit Recognition

Abstract

Computer Vision and Machine Learning are two domains that are upcoming and helpful in the modern era. Computer Vision is a science that is designed to try to make a computer as good as a human. Machine Learning helps improve computer vision by training it to improve every time it is used. This paper presents a model of Support Vector Machine (SVM) with the AdaBoost classifier, which has proven results in recognizing different types of patterns. In this model, SVM is used as a recognizer. This model automatically extracts features from the raw images and generates predictions. The results are subject to experiments conducted on the well-known MNIST digit database.

References

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Published

2020-07-31
CITATION
DOI: 10.26438/ijcse/v8i7.162165
Published: 2020-07-31

How to Cite

[1]
A. Naik and V. Gaikwad, “Handwritten Digit Recognition Using Support Vector Machine”, Int. J. Comp. Sci. Eng., vol. 8, no. 7, pp. 162–165, Jul. 2020.

Issue

Vol. 8 No. 7 (2020): IJCSE July Edition

Section

Research Article

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