Fingerspelling - Indian Sign Language Training Tool

Nanaware, Tejas; Sahasrabudhe, Sameer; Ayer, Nitin; Christo, Romey

doi:10.1109/icalt.2018.00083

Cited by 9 publications

(1 citation statement)

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“…Developing tools to translate sign language into text or voice to help nondeaf and dumb individuals communicate is crucial. Many sign language recognition systems are based on Indian, Japanese, British, Brazilian, Chinese, Australian, and American sign languages [19][20][21][22][23][24][25][26][27][28][29][30]. However, sign language research has also focused on Asian [31], English, Turkish [32], and German sign languages [33], as well as Arabic, albeit without achieving the desired results in terms of system performance.…”

Section: Introductionmentioning

confidence: 99%

Integrated Mediapipe with a CNN Model for Arabic Sign Language Recognition

Moustafa,

Mohd Rahim,

Bouallegue

et al. 2023

Journal of Electrical and Computer Engineering

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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.

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