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Comprehensive Survey of Performance of Techniques for Hand Gesture Recognition System for Sign Languages

Author

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  • Mayuri Murkute
  • Jayshree R. Pansare

Abstract

Hand Gesture Recognition System (HGRS) used for human-computer interaction. HGRS is having phases which are applied on captured image. The image is passes through phases like Hand Detection, Region of Extraction, Feature Extraction, Feature Matching, Pattern Recognition. There are many algorithms and techniques used for the phases in HGRS. There are various techniques used to improve the performance of HGRS in feature extraction and feature matching. This comprehensive study of techniques used for feature extraction and feature matching will elaborate performance of these techniques for various purpose in HGRS. For the various phases in HGRS, different algorithms can be used for feature extraction and feature matching like K-nearest neighbor, Support Vector Machine, BOF and SIFT, etc. The performances of the various existing algorithms is discussed.

Suggested Citation

  • Mayuri Murkute & Jayshree R. Pansare, 2017. "Comprehensive Survey of Performance of Techniques for Hand Gesture Recognition System for Sign Languages," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 2(6), pages 1137-1140, December.
    • Handle: RePEc:jbh:ijsrcs:v2:y2017:i6:id:hcseit11726310
    Note: Article URL: https://ijsrcseit.com/CSEIT11726310
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