Arabic sign language recognition using optical flow-based features and HMM

Ala addin I. Sidig, Hamzah Luqman*, Sabri A. Mahmoud

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

17 Scopus citations


Sign language is the main communication channel of deaf community. It uses gestures and body language such as facial expressions, lib patterns, and hand shapes to convey meaning. Sign language differs from one country to another. Sign language recognition helps in removing barriers between people who understand only spoken language and those who understand sign language. In this work, we propose an algorithm for segmenting videos of signs into sequences of still images and four techniques for Arabic sign language recognition, namely Modified Fourier Transform (MFT), Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG), and combination of HOG and Histogram of Optical Flow (HOG-HOF). These techniques are evaluated using Hidden Markov Model (HMM). The best performance is obtained with MFT features with 99.11% accuracy. This recognition rate shows the correctness and robustness of the proposed signs’ video segmentation algorithm.

Original languageEnglish
Title of host publicationLecture Notes on Data Engineering and Communications Technologies
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages9
StatePublished - 2018

Publication series

NameLecture Notes on Data Engineering and Communications Technologies
ISSN (Print)2367-4512
ISSN (Electronic)2367-4520

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2018.


  • Arabic sign language recognition
  • HMM
  • HOG
  • Video segmentation

ASJC Scopus subject areas

  • Media Technology
  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Computer Networks and Communications
  • Information Systems


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