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An Automatic Arabic Sign Language Recognition System (ArSLRS)

المصدر: مجلة جامعة الملك سعود - علوم الحاسب والمعلومات
الناشر: جامعة الملك سعود
المؤلف الرئيسي: Ibrahim, Nada B. (Author)
مؤلفين آخرين: Selim, Mazen M. (Co-Author) , Zayed, Hala H. (Co-Author)
المجلد/العدد: مج30, ع4
محكمة: نعم
الدولة: السعودية
التاريخ الميلادي: 2018
الصفحات: 470 - 477
DOI: 10.33948/0584-030-004-004
ISSN: 1319-1578
رقم MD: 974475
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: science
مواضيع:
كلمات المؤلف المفتاحية:
Gesture Recognition | Arabic Sign Language Recognition | Isolated Word Recognition | Image Based Recognition
رابط المحتوى:
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LEADER 02333nam a22002537a 4500
001 1717260
024 |3 10.33948/0584-030-004-004 
041 |a eng 
044 |b السعودية 
100 |9 525672  |a Ibrahim, Nada B.  |e Author 
245 |a An Automatic Arabic Sign Language Recognition System (ArSLRS) 
260 |b جامعة الملك سعود  |c 2018 
300 |a 470 - 477 
336 |a بحوث ومقالات  |b Article 
520 |b  Sign language recognition system (SLRS) is one of the application areas of human computer interaction (HCI) where signs of hearing impaired people are converted to text or voice of the oral language. This paper presents an automatic visual SLRS that translates isolated Arabic words signs into text. The proposed system has four main stages: hand segmentation, tracking, feature extraction and classification. A dynamic skin detector based on the face color tone is used for hand segmentation. Then, a proposed skin-blob tracking technique is used to identify and track the hands. A dataset of 30 isolated words that used in the daily school life of the hearing-impaired children was developed for evaluating the proposed system, taking into consideration that 83% of the words have different occlusion states. Experimental results indicate that the proposed system has a recognition rate of 97% in signer-independent mode. In addition to, the proposed occlusion resolving technique can outperform other methods by accurately specify the position of the hands and the head with an improvement of 2.57% at s = 5 that aid in differentiating between similar gestures. 
653 |a لغة الإشارة  |a لغة الإشارة العربية  |a اللغة الشفوية  |a برامج الحاسوب 
692 |b Gesture Recognition  |b  Arabic Sign Language Recognition  |b Isolated Word Recognition  |b Image Based Recognition 
700 |9 525674  |a Selim, Mazen M.  |e Co-Author 
700 |9 525679  |a Zayed, Hala H.  |e Co-Author 
773 |c 004  |e Journal of King Saud University (Computer and Information Sciences)  |f Maǧalaẗ ǧamʼaẗ al-malīk Saud : ùlm al-ḥasib wa al-maʼlumat  |l 004  |m مج30, ع4  |o 0584  |s مجلة جامعة الملك سعود - علوم الحاسب والمعلومات  |v 030  |x 1319-1578 
856 |u 0584-030-004-004.pdf 
930 |d y  |p y 
995 |a science 
999 |c 974475  |d 974475 

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