TY - GEN
T1 - An Efficient Model for American Sign Language Recognition Using Deep-Neural Networks
AU - Chandra, Avtar
AU - Ranjan, Aditya
AU - Sahu, Dinesh Prasad
AU - Prakash, Shiv
AU - Yang, Tiansheng
AU - Rathore, Rajkumar Singh
AU - Vajpayee, Abhay
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2025/1/17
Y1 - 2025/1/17
N2 - The Sign Language is a way that is used for communication by people with inability to speak and hear. Thus, improving these languages is recognized as being widely influential across society. Worldwide, about 7,000 sign languages are used for communication, and many studies have been performed using different sign languages. This study considers American Sign Language (ASL) due to its popularity. We have proposed an efficient model using deep learning for 26 alphabets hand gestures in ASL to communicate with people. The proposed model has been assessed with the benchmark dataset and compared with many studies using the same datasets. It has achieved the highest accuracy as compared to the contemporary model. It has been observed that working with complex numbers positively impacted performance by approximately 20% compared to configuring our model to work with real numbers while keeping its structure intact.
AB - The Sign Language is a way that is used for communication by people with inability to speak and hear. Thus, improving these languages is recognized as being widely influential across society. Worldwide, about 7,000 sign languages are used for communication, and many studies have been performed using different sign languages. This study considers American Sign Language (ASL) due to its popularity. We have proposed an efficient model using deep learning for 26 alphabets hand gestures in ASL to communicate with people. The proposed model has been assessed with the benchmark dataset and compared with many studies using the same datasets. It has achieved the highest accuracy as compared to the contemporary model. It has been observed that working with complex numbers positively impacted performance by approximately 20% compared to configuring our model to work with real numbers while keeping its structure intact.
KW - American Sign Language (ASL)
KW - Artificial Intelligence (AI)
KW - Deep Learning
KW - Feature Extraction
UR - http://www.scopus.com/inward/record.url?scp=85217196455&partnerID=8YFLogxK
U2 - 10.1109/dasa63652.2024.10836568
DO - 10.1109/dasa63652.2024.10836568
M3 - Conference contribution
SN - 979-8-3503-6911-3
T3 - 2024 International Conference on Decision Aid Sciences and Applications (DASA)
SP - 1
EP - 5
BT - 2024 International Conference on Decision Aid Sciences and Applications (DASA)
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 2024 International Conference on Decision Aid Sciences and Applications (DASA)
Y2 - 11 December 2024 through 12 December 2024
ER -