Integrated Mediapipe with a CNN Model for Arabic Sign Language Recognition

被引:4
作者
AL Moustafa, Ahmad M. J. [1 ,2 ]
Rahim, Mohd Shafry Mohd [1 ,3 ]
Bouallegue, Belgacem [2 ,4 ]
Khattab, Mahmoud M. [2 ]
Soliman, Amr Mohmed [5 ]
Tharwat, Gamal [6 ]
Ahmed, Abdelmoty M. [2 ,6 ]
机构
[1] Univ Teknol Malaysia, Fac Comp, Johor Baharu 81310, Johor, Malaysia
[2] King Khalid Univ, Coll Comp Sci, Abha, Saudi Arabia
[3] Univ Teknol Malaysia, Media & Game Innovat Ctr Excellence, Inst Human Ctr Engn, Johor Baharu 81310, Johor, Malaysia
[4] Univ Monastir, Fac Sci Monastir, Elect & Microelect Lab EEL, Monastir, Tunisia
[5] King Khalid Univ, Dept Special Educ, Coll Educ, Abha, Saudi Arabia
[6] Al Azhar Univ, Dept Syst & Computers Engn, Fac Engn, Cairo, Egypt
关键词
Audition - Character recognition - Deep neural networks;
D O I
10.1155/2023/8870750
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Deaf and dumb people struggle with communicating on a day-to-day basis. Current advancements in artificial intelligence (AI) have allowed this communication barrier to be removed. A letter recognition system for Arabic sign language (ArSL) has been developed as a result of this effort. The deep convolutional neural network (CNN) structure is used by the ArSL recognition system in order to process depth data and to improve the ability for hearing-impaired to communicate with others. In the proposed model, letters of the hand-sign alphabet and the Arabic alphabet would be recognized and identified automatically based on user input. The proposed model should be able to identify ArSL with a rate of accuracy of 97.1%. In order to test our approach, we carried out a comparative study and discovered that it is able to differentiate between static indications with a higher level of accuracy than prior studies had achieved using the same dataset.
引用
收藏
页数:15
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