Skip to main content
  • Research Article
  • Open access
  • Published:

Telescopic Vector Composition and Polar Accumulated Motion Residuals for Feature Extraction in Arabic Sign Language Recognition


This work introduces two novel approaches for feature extraction applied to video-based Arabic sign language recognition, namely, motion representation through motion estimation and motion representation through motion residuals. In the former, motion estimation is used to compute the motion vectors of a video-based deaf sign or gesture. In the preprocessing stage for feature extraction, the horizontal and vertical components of such vectors are rearranged into intensity images and transformed into the frequency domain. In the second approach, motion is represented through motion residuals. The residuals are then thresholded and transformed into the frequency domain. Since in both approaches the temporal dimension of the video-based gesture needs to be preserved, hidden Markov models are used for classification tasks. Additionally, this paper proposes to project the motion information in the time domain through either telescopic motion vector composition or polar accumulated differences of motion residuals. The feature vectors are then extracted from the projected motion information. After that, model parameters can be evaluated by using simple classifiers such as Fisher's linear discriminant. The paper reports on the classification accuracy of the proposed solutions. Comparisons with existing work reveal that up to 39% of the misclassifications have been corrected.



  1. Assaleh K, Al-Rousan M: Recognition of Arabic sign language alphabet using polynomial classifiers. EURASIP Journal on Applied Signal Processing 2005,2005(13):2136-2145. 10.1155/ASP.2005.2136

    Article  MATH  Google Scholar 

  2. Shanableh T, Assaleh K, Al-Rousan M: Spatio-temporal feature-extraction techniques for isolated gesture recognition in Arabic sign language. IEEE Transactions on Systems, Man, and Cybernetics, Part B 2007,37(3):641-650. 10.1109/TSMCB.2006.889630

    Article  Google Scholar 

  3. Chen F-S, Fu C-M, Huang C-L: Hand gesture recognition using a real-time tracking method and hidden Markov models. Image and Vision Computing 2003,21(8):745-758. 10.1016/S0262-8856(03)00070-2

    Article  Google Scholar 

  4. Yang M-H, Ahuja N, Tabb M: Extraction of 2D motion trajectories and its application to hand gesture recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002,24(8):1061-1074. 10.1109/TPAMI.2002.1023803

    Article  Google Scholar 

  5. Starner T, Weaver J, Pentland A: Real-time American sign language recognition using desk and wearable computer based video. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998,20(12):1371-1375. 10.1109/34.735811

    Article  Google Scholar 

  6. Sharjah City for Humanitarian Services (SCHS)

  7. Jain JR, Jain AK: Displacement measurement and its application in interframe image coding. IEEE Transactions on Communications 1981,29(12):1799-1808. 10.1109/TCOM.1981.1094950

    Article  Google Scholar 

  8. Ghanbari M: The cross-search algorithm for motion estimation. IEEE Transactions on Communications 1990,38(7):950-953. 10.1109/26.57512

    Article  Google Scholar 

  9. Xi YL, Haoa CH-Y, Fana YY, Hua HQ: A fast block-matching algorithm based on adaptive search area and its VLSI architecture for H.264/AVC. Signal Processing: Image Communication 2006,21(8):626-646. 10.1016/j.image.2006.05.001

    Google Scholar 

  10. Ghanbari M: Video Coding: An Introduction to Standard Codecs, IEE Telecommunication Series 42. Institution Electrical Engineers, London, UK; 1999.

    Google Scholar 

  11. Chen W-H, Pratt W: Sense adaptive coder. IEEE Transactions on Communications 1984,32(3):225-232. 10.1109/TCOM.1984.1096066

    Article  Google Scholar 

  12. Shanableh T, Ghanbari M: Heterogeneous video transcoding to lower spatio-temporal resolutions and different encoding formats. IEEE Transactions on Multimedia 2000,2(2):101-110. 10.1109/6046.845014

    Article  Google Scholar 

  13. Gonzalez R, Woods R: Digital Image Processing. 2nd edition. Prentice Hall, Upper Saddle River, NJ, USA; 2002.

    Google Scholar 

  14. Assaleh K, Shanableh T, Hajjaj H: Online video-based handwritten arabic alphabet recognition. The 3rd AUS International Symposium on Mechatronics (AUS-ISM '06), April 2006, Sharjah, UAE

    Google Scholar 

  15. Rabiner LR: Tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 1989,77(2):257-286. 10.1109/5.18626

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to T Shanableh.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Shanableh, T., Assaleh, K. Telescopic Vector Composition and Polar Accumulated Motion Residuals for Feature Extraction in Arabic Sign Language Recognition. J Image Video Proc 2007, 087929 (2007).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: