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- W4386002145 abstract "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." @default.
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- W4386002145 date "2023-08-19" @default.
- W4386002145 modified "2023-10-12" @default.
- W4386002145 title "Integrated Mediapipe with a CNN Model for Arabic Sign Language Recognition" @default.
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- W4386002145 doi "https://doi.org/10.1155/2023/8870750" @default.
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