Arabic sign language recognition
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Arabic Sign Language Recognition. Mohamed Mohandes King Fahd University of Petroleum and Minerals [email protected] OUTILNE. Introduction Sign Language Recognition Translation of text to sign language Conclusion and future work. Importance of Sign Language.

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Arabic Sign Language Recognition

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Arabic sign language recognition

Arabic Sign Language Recognition

Mohamed Mohandes

King Fahd University of Petroleum and Minerals

[email protected]


Outilne

OUTILNE

  • Introduction

  • Sign Language Recognition

  • Translation of text to sign language

  • Conclusion and future work


Importance of sign language

Importance of Sign Language

  • Arabic sign language (ARSL) is different from spoken Arabic language in terms of grammars, vocabulary, and delivery.

  • ARSL is the natural language for deaf like spoken language to vocal

  • Sign language is different from country to other (Australia, UK, USA)

  • 100,000 deaf and hearing impaired in KSA


Objectives

Objectives

Using Computers to make life of deaf easier and integrating them in the society :

  • Translating ARSL to spoken language

  • Translating Arabic speech to ARSL


Arsl recognition

ARSL Recognition

  • Image based

    • Requires special set up for camera

    • Heavy computational load to extract hands

  • Electronic-Glove based

    • Inconvenience of gloves

    • Ease of signal extractions


Translating arsl to speech

Translating ARSL to Speech

سلمان


System components

System Components


Cyberglove

CyberGlove


Cyberglove1

CyberGlove

  • 22 sensors

  • Light weight

  • Flexible


Arabic sign language recognition

Tracking System


Arabic sign language recognition

Sensor signals for two different words


Arabic sign language recognition

Coordinates of wordمع السلامة

Coordinates of word الله


Arabic sign language recognition

الاشارات المعتمدة

المنظمة العربية

للتربية والثقافة والعلوم

الاتحاد العربي

للهيئات العاملة في رعاية الصم


Arabic sign language recognition

1300 signs

344 single

handed


Arabic sign language recognition

Data Collection

344 single handed signs

6880 Samples

6880 samples

20 samples from every sign: 15 for training and 5 for testing


Support vector machine

Support Vector Machine


Arabic sign language recognition

SVM


System performance

System Performance

  • Time segments and Principle Component Analysis for feature extraction

  • Correct recognition rate of 98.33%


Analysis of misclassified signs

Analysis of Misclassified Signs

Frames of sign of letter “س”

Frames of sign of letter “ش”


Analysis of misclassified signs1

Analysis of Misclassified Signs

Frames from the sign “employee”

Frames from the sign “Down syndrome”


Analysis in feature space

Analysis in Feature space

Hand shape of “employee”

Hand shape of “Down syndrome”


Translating speech to arsl

Translating Speech to ARSL

سلمان

ســـــــــــلمان


Conclusions and future work

Conclusions and Future work

  • Developed a Real-time single-handed Arabic sign language recognition system with accuracy of 98.33%

  • Working on two-handed signs recognition

  • Developing our own smart glove for Arabic Sign Language

  • Mapping of signs to roots for speech to sign translation


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