Sign Language Recognition using Principal Component Analysis and Support Vector Machine

  • Astri Novianty Department of Computer Engineering, Telkom University, Indonesia http://orcid.org/0000-0002-8109-5544
  • Fairuz Azmi Department of Computer Engineering, Telkom University, Indonesia

Abstract

The World Health Organization (WHO) estimates that over five percent of the world's population are hearing-impaired. One of the communication problems that often arise between deaf or speech impaired with normal people is the low level of knowledge and understanding of the deaf or speech impaired's normal sign language in their daily communication. To overcome this problem, we build a sign language recognition system, especially for the Indonesian language. The sign language system for Bahasa Indonesia, called Bisindo, is unique from the others. Our work utilizes two image processing algorithms for the pre-processing, namely the grayscale conversion and the histogram equalization. Subsequently, the principal component analysis (PCA) is employed for dimensional reduction and feature extraction. Finally, the support vector machine (SVM) is applied as the classifier. Results indicate that the use of the histogram equalization significantly enhances the accuracy of the recognition. Comprehensive experiments by applying different random seeds for testing data confirm that our method achieves 76.8% accuracy. Accordingly, a more robust method is still open to enhance the accuracy in sign language recognition.

Downloads

Download data is not yet available.
Published
2021-03-17
How to Cite
NOVIANTY, Astri; AZMI, Fairuz. Sign Language Recognition using Principal Component Analysis and Support Vector Machine. IJAIT (International Journal of Applied Information Technology), [S.l.], p. 49-56, mar. 2021. ISSN 2581-1223. Available at: <//journals.telkomuniversity.ac.id/ijait/article/view/3015>. Date accessed: 02 mar. 2024. doi: https://doi.org/10.25124/ijait.v4i01.3015.
Section
Articles