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Received 19 November 2022, accepted 12 December 2022, date of publication 2 January 2023, date of current version 9 January 2023. Digital Object Identifier 10.1 109/ACCESS.2022.3233671 Multi-Semantic Discriminative Feature Learning for Sign Gesture Recognition Using Hybrid Deep Neural Architecture E. RAJALAKSHMI1, R. ELAKKIYA 1,2, V. SUBRAMANIYASWAMY 1, L

E. Rajalakshmi et al.: Multi-Semantic Discriminative Feature Learning for Sign Gesture Recognition fh =f ( wf 1Xt +wf 2H(t−1) ) (10) rh =f ( wr1Xt +wr2H(t+1) ) (11) Bt =g (wO ∗fh +wO ∗rh) (12) The Attention layer is employed based on the signiﬁ- cance of the sign gesture’s sequential and temporal essence. Eq. 13 to Eq

. V. CONCLUSION AND FUTURE WORK A novel Sign Language Recognition system has been devel- oped by deploying a multi-semantic discriminative feature learning Deep Neural Net and spatial, temporal and sequen- tial feature learning. The proposed hDNN-SLR framework intends to extract semantic manual co-articulations and non- manual elements, which are the key components necessary for sign recognition. In addition, spatial, sequential, and temporal features are also considered for accurate recogni- tion. Furthermore, abstract and discriminative feature extrac- tion is also carried out to segregate genuine and non-useful gestures

E. Rajalakshmi et al.: Multi-Semantic Discriminative Feature Learning for Sign Gesture Recognition KETAN KOTECHAis currently an Administrator and a Teacher of deep learning. He has exper- tise and experience in cutting-edge research and projects in AI and deep learning for the last 25 years. He has published over 100 widely in sev- eral excellent peer-reviewed journals on various topics ranging from cutting-edge AI, education policies, teaching-learning practices, and AI for all. His research interests include artiﬁcial intel- ligence, computer algorithms, machine learning, and deep learning

E. Rajalakshmi et al.: Multi-Semantic Discriminative Feature Learning for Sign Gesture Recognition FIGURE 8. Performance of framework having trained with the multi-signer dataset with three different signer datasets and testing with the multi-signer dataset. FIGURE 9. Performance of framework having trained with the multi-signer dataset with five different signer datasets and validation with the multi-signer dataset. the third constraint. When comparing the three graphs with different multi-signer train-test ratios, we can see that the framework performs well and tends to attain convergence when the Network is well-trained with a multi-signer dataset. E

E. Rajalakshmi et al.: Multi-Semantic Discriminative Feature Learning for Sign Gesture Recognition system (SLR). Recently developed Sign Language datasets include American, Arabic [1], German, Chinese [2], Turkish [3], Bhutanese [4], Russian, and Indian Sign languages (ISL) [5]. SLR has been intensively investigated to help hearing people comprehend sign language and make the everyday lives of the speech-impaired and hard-of-hearing community more convenient. The SLR frameworks aim to detect and rec- ognize sign language performed by the sign interpreter from a visual medium

E. Rajalakshmi et al.: Multi-Semantic Discriminative Feature Learning for Sign Gesture Recognition [43] D. Li, C. R. Opazo, X. Yu, and H. Li, ‘‘Word-level deep sign language recognition from video: A new large-scale dataset and methods compari- son,’’ in Proc. IEEE Winter Conf. Appl. Comput. Vis. (WACV), Mar. 2020, pp. 1459–1469. [44] M. Maruyama, S. Ghose, K. Inoue, P. P. Roy, M. Iwamura, and M. Yoshioka, ‘‘Word-level sign language recognition with multi-stream neural networks focusing on local regions,’’ 2021, arXiv:2106.15989. [45] A. A. Hosain, P. Selvam Santhalingam, P. Pathak, H. Rangwala, and J. Kosecka, ‘‘Hand pose guided 3D pooling for word-level sign language recognition,’’ inProc

E. Rajalakshmi et al.: Multi-Semantic Discriminative Feature Learning for Sign Gesture Recognition in real-world situations. As a part of future work, we would like to extend our study toward continuous sign sentence recognition. We would also like to design a framework for handling the segmentation ambiguities and moment epenthe- sis in continuous sign language recognition. The isolated word gesture recognition framework could also be integrated to enhance sign spotting from continuous sign video stream for recognition sign sentences from continuous sign gestures. We also intend to increase the dataset and publicly publish it for further research

Question: who are the authors of the paper "Multi-Semantic Discriminative Feature Learning
 for Sign Gesture Recognition Using
 Hybrid Deep Neural Architecture"

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