Towards Arabic Alphabet and Numbers Sign Language Recognition

Ahmad Hasasneh
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Towards Arabic Alphabet and Numbers Sign Language Recognition

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Abstract

This paper proposes to develop a new Arabic sign language recognition using Restricted Boltzmann Machines and a direct use of tiny images. Restricted Boltzmann Machines are able to code images as a superposition of a limited number of features taken from a larger alphabet. Repeating this process in deep architecture (Deep Belief Networks) leads to an efficient sparse representation of the initial data in the feature space. A complex problem of classification in the input space is thus transformed into an easier one in the feature space. After appropriate coding, a softmax regression in the feature space must be sufficient to recognize a hand sign according to the input image. To our knowledge, this is the first attempt that tiny images feature extraction using deep architecture is a simpler alternative approach for Arabic sign language recognition that deserves to be considered and investigated.

References

48 Cites in Article
  1. N Pashaloudi,K Margaritis (1112). Hidden markov model for sign language recognition: A review.
  2. L Dipietro,A Sabatini,P Dario (2008). A survey of glove-based systems and their applications.
  3. M Moni (2009). Hmm based hand gesture recognition: A review on techniques and approaches.
  4. S Kausar,M Javed (2011). A survey on sign language recognition.
  5. H Riad,S Elmonier,A Shohieb,Asem (2012). SignsWorld: Deeping into the Silence World and Hearing Its Signs (State of the Art).
  6. P Vijay,N Suhas,C Chandrashekhar,D Dhananjay (2012). Recent developments in sign language recognition: a review.
  7. M Mohandes,M Deriche,J Liu (2014). Image-Based and Sensor-Based Approaches to Arabic Sign Language Recognition.
  8. N Salleh,J Jais,L Mazalan,R Ismail,S Yussof,A Ahmad,A Anuar,D Mohamad (2006). Sign language to voice recognition: hand detection techniques for vision-based approach.
  9. R Naoum,H Owaied,S Joudeh (2012). Development of a new Arabic sign language recognition using k-nearest neighbor algorithm.
  10. Mohamed Mohandes (2013). Recognition of Two-Handed Arabic Signs Using the CyberGlove.
  11. Ahmed Samirelons,Magdy Abull‐ela,Mohamed Tolba (2013). Pulse‐coupled neural network feature generation model for Arabic sign language recognition.
  12. M Mohandes,M Deriche (2013). Arabic sign language recognition by decisions fusion using Dempster-Shafer theory of evidence.
  13. Khaled Assaleh,Tamer Shanableh,Mohammed Zourob (2012). Low Complexity Classification System for Glove-Based Arabic Sign Language Recognition.
  14. H Khaled,S Sayed,E Saad,H Ali (2015). Hand gesture recognition using modified 1$ and background subtraction algorithms.
  15. A Galvis,B Batoczki,I Pecson,T Sellers,E Goroza,C Nakamura (2001). A domestic case of pediatric miliary tuberculosis.
  16. Omar Al-Jarrah,Alaa Halawani (2001). Recognition of gestures in Arabic sign language using neuro-fuzzy systems.
  17. M Al-Rousan,M Hussain (2001). Automatic recognition of arabic sign language finger spelling.
  18. O Al-Jarrah,F Al-Omari (2007). Improving gesture recognition in the arabic sign language using texture analysis.
  19. M Maraqa,R Abu-Zaiter (2008). Recognition of arabic sign language (arsl) using recurrent neural networks.
  20. M Maraqa,F Al-Zboun,M Dhyabat,R Zitar (2012). Recognition of Arabic sign language (arsl) using recurrent neural networks.
  21. E Hemayed,A Hassanien (2010). Edge-based recognizer for Arabic sign language alphabet (ars2v-arabic sign to voice).
  22. S Elons,M Abull-Ela,M Tolba (2013). Neutralizing lighting nonhomogeneity and background size in pcnn image signature for Arabic sign language recognition.
  23. M Samirelons,M Abull-Ela,Tolba (2013). Pulsecoupled neural network feature generation model for arabic sign language recognition.
  24. Z Mahmoud,M Alaa,A -R,M Sameh,Elsayed (2015). Arabic alphabet and numbers sign language recognition.
  25. M Mohandes,S Aliyu,M Deriche (2014). Arabic sign language recognition using the leap motion controller.
  26. D Olshausen,Field (2004). Sparse coding of sensory inputs.
  27. J Field (1994). What is the goal of sensory coding?.
  28. Marc'aurelio Ranzato,Fu Huang,Y.-L Boureau,Yann Lecun (2007). Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition.
  29. J Yang,K Yu,Y Gong,T Huang (2009). Linear spatial pyramid matching using sparse coding for image classification.
  30. John Wright,Yi Ma,Julien Mairal,Guillermo Sapiro,Thomas Huang,Shuicheng Yan (2010). Sparse Representation for Computer Vision and Pattern Recognition.
  31. Y.-L Boureau,F Bach,Y Lecun,J Ponce (2010). Learning mid-level features for recognition.
  32. E Hasasneh,P Frenoux,Tarroux (2012). Semantic Place Recognition based on Deep Belief Networks and Tiny Images.
  33. K Labusch,T Martinetz (2010). Learning sparse codes for image reconstruction.
  34. Geoffrey Hinton,Simon Osindero,Yee-Whye Teh (2006). A Fast Learning Algorithm for Deep Belief Nets.
  35. P Smolensky (1987). Information processing in dynamical systems: Foundations of harmony theory.
  36. G Hinton (2002). Training products of experts by minimizing contrastive divergence.
  37. (2007). The arabic dictionary of gestures for the deaf.
  38. G Krizhevsky,Hinton (2010). Factored 3-way restricted Boltzmann machines for modeling natural images.
  39. Honglak Lee,Roger Grosse,Rajesh Ranganath,Andrew Ng (2009). Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations.
  40. Mohammad Norouzi,Mani Ranjbar,Greg Mori (2009). Stacks of convolutional Restricted Boltzmann Machines for shift-invariant feature learning.
  41. F Attneave (1954). Some informational aspects of visual perception.
  42. H Barlow (2001). Redundancy reduction revisited.
  43. A Coates,H Ng,Lee (2011). An analysis of singlelayer networks in unsupervised feature learning.
  44. Geoffrey Hinton,Alex Krizhevsky,Sida Wang (2011). Transforming Auto-Encoders.
  45. Geoffrey Hinton (2009). Deep belief networks.
  46. Krizhevsky (2009). Learning multiple layers of features from tiny images.
  47. G Hinton (2010). A practical guide to training restricted boltzmann machines.
  48. Antonio Torralba,Rob Fergus,Yair Weiss (2008). Small codes and large image databases for recognition.
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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Ahmad Hasasneh. 2017. "Towards Arabic Alphabet and Numbers Sign Language Recognition". Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 17 (GJCST Volume 17 Issue F2).

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
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Classification
GJCST-F Classification I.5
I.7.5
  • Submission ReceivedDecember 8, 2016
  • Peer Review Double Blind
    Handling Editor
  • Accepted December 31, 2016
  • Published January 15, 2017
Version of record

v1.2

Issue date
October 21, 2017

Language
English
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Towards Arabic Alphabet and Numbers Sign Language Recognition

Ahmad Hasasneh
Ahmad Hasasneh Palestine Ahliya University
  • Submission ReceivedDecember 8, 2016
  • Peer Review Double Blind
    Handling Editor
  • Accepted December 31, 2016
  • Published January 15, 2017