An Approach on Building Communicative Channel for Hand Sign Translation to Text and Speech Model
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-4700-9_11
Reference11 articles.
1. Hu, H., Zhao, W. Zhou, W., Li, H.: SignBERT+: hand-model-aware self-supervised pre-training for sign language understanding. IEEE Trans. Pattern Anal. Mach. Intell. 45(9), 11221–11239 (2023). https://doi.org/10.1109/TPAMI.2023.3269220
2. Wei, C., Zhao, J., Zhou, W., Li, H.: Semantic boundary detection with reinforcement learning for continuous sign language recognition. IEEE Trans. Circuits Syst. Video Technol. 31(3), 1138–1149 (2021). https://doi.org/10.1109/TCSVT.2020.2999384
3. Zhao, T., Liu, J., Wang, Y., Liu, H, Chen, Y.: Towards low-cost sign language gesture recognition leveraging wearables. IEEE Trans. Mobile Comput. 20(4):1685–1701 (2021). https://doi.org/10.1109/TMC.2019.2962760
4. Ben Atitallah, B., et al.: Hand sign recognition system based on EIT imaging and robust CNN classification. IEEE Sens. J. 22(2):1729–1737 (2022). https://doi.org/10.1109/JSEN.2021.3130982.
5. Saleh Y., Issa, G.F.: Arabic sign language recognition through deep neural networks fine-tuning. Int. J. Online Biomed. Eng. (iJOE) 16(05), 71 (2020). https://doi.org/10.3991/ijoe.v16i05.13087 (International Association of Online Engineering (IAOE))
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