Intelligent real-time Arabic sign language classification using attention-based inception and BiLSTM

• Published in: Computers & electrical engineering Vol. 95; p. 107395
• Main Authors: Abdul, Wadood, Alsulaiman, Mansour, Amin, Syed Umar, Faisal, Mohammed, Muhammad, Ghulam, Albogamy, Fahad R., Bencherif, Mohamed A., Ghaleb, Hamid
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.10.2021; Elsevier BV
• Subjects: Artificial neural networks; BiLSTM; Bio-inspired computing; Biomimetics; Classification; Deep learning; Feature extraction; Inception; Machine learning; Real time; Real-time classification; Sign language; Video data; ICMEW; CNN; ICIP; LSTM; DL; CVPR; BiLSTM; ICCVW; Deep learning; Sign language; ECCV; SGD; Real-time classification; Abbreviations: ArSL; RNN; Inception; ASL; Bio-inspired computing; ICPR

•Bio-Inspired novel attention-based inception architecture is proposed that can adapt to different types of spatial contexts using convolution filters of different sizes. The characteristics of each dataset are unique, hence the attention mechanism helps focus on those features to improve classifica...