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Towards Arabic Alphabet and Numbers Sign Language Recognition

§ Palestine Ahliya University

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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.

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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
Classification
GJCST-F Classification I.5
I.7.5
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