Using gabor filter bank with downsampling and SVM for visual sign language alphabet recognition

Galal M. Bin Makhashen, Hamzah A. Luqman, El Sayed M. El-Alfy*

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

2 Scopus citations


With the increasing advances in computer vision, the research on automated human gesture and sign language has attracted the attention of many researchers. It has many applications for human-computer interaction helping persons with hearing impairment in smart environments. In this paper, we focus on static hand visual features to build a system for recognizing hand and finger gestures representing different sign language alphabets. After hand segmentation, the proposed method employs texture based features extracted by down-sampling Gabor-transformed images using multiple scales and orientations. Then, a support vector machine is used for multi-class classification. The evaluation of the proposed approach on a benchmark dataset for the American sign language has reported over 95% overall accuracy with several signs perfectly recognized.

Original languageEnglish
StatePublished - 2019

Bibliographical note

Publisher Copyright:
© 2019 Institution of Engineering and Technology. All rights reserved.


  • Downsampling
  • Gabor Filer Bank
  • Gesture Recognition
  • Pattern Recognition
  • Sign Language
  • Support Vector Machine
  • Texture analysis

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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