Isolated Sign Language Recognition Using Deep Learning
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-58181-6_29
Reference17 articles.
1. Al-Hammadi, M., Muhammad, G., Abdul, W., Alsulaiman, M., Bencherif, M.A., Mekhtiche, M.A.: Hand gesture recognition for sign language using 3DCNN. IEEE Access 8, 79491–79509 (2020). https://doi.org/10.1109/ACCESS.2020.2990434
2. Al-Qurishi, M., Khalid, T., Souissi, R.: Deep learning for sign language recognition: current techniques, benchmarks, and open issues. IEEE Access 9, 126917–126951 (2021). https://doi.org/10.1109/ACCESS.2021.3110912
3. Aly, S.K.H., Aly, W.: DeepArSLR: a novel signer-independent deep learning framework for isolated Arabic sign language gestures recognition. IEEE Access 8, 83199–83212 (2020)
4. Botros, F.S., Phinyomark, A., Scheme, E.J.: Electromyography-based gesture recognition: is it time to change focus from the forearm to the wrist? IEEE Trans. Industr. Inf. 18(1), 174–184 (2020)
5. Dawod, A.Y., Chakpitak, N.: Novel technique for isolated sign language based on fingerspelling recognition. In: 2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), pp. 1–8. IEEE (2019)
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