Principal Component Analysis and Support Vector Machine approach for Gujarati Handwritten Numeral Recognition

Authors

  • Dr. Mrs. Mamta Jagdish Baheti  Department of Computer Science, Hislop College, Nagpur, Maharashtra, India

Keywords:

Principal Component Analysis, Support Vector Machine, Gujarati, Handwritten, Numeral Recognition

Abstract

In this paper we have proposed an algorithm for recognition of handwritten Gujarati Numerals. While reviewing the reported work, it was found that Gujarati is used all across globe including India. The proposed algorithm is applied to noisy numerals. In the algorithm we have used invariant moments as feature extraction technique and PCA and SVM as classifiers. We compared the results for both the classifiers and found that for our database SVM gave 90.55% which were better results as compared to by PCA 80.6% for Invariant moments as feature extraction technique. These results can be improved over good quality images.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-04-30

Issue

Volume 3, Issue 3, March-April-2018

Section

Research Articles

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How to Cite

[1]
Dr. Mrs. Mamta Jagdish Baheti, " Principal Component Analysis and Support Vector Machine approach for Gujarati Handwritten Numeral Recognition, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.2139-2147, March-April-2018.