ARCHIVES

1. W. Sandler and D. Lillo-Martin, Sign language and linguistic universals. Cambridge University Press, 2006.
2. Z. Yang, Z. Shi, X. Shen, and Y.-W. Tai, “Sf-net: Structured feature network for continuous sign language recognition,” arXiv preprint
arXiv: 1908.01341, 2019.
3. O. Koller, J. Forster, and H. Ney, “Continuous sign language recognition: Towards large vocabulary statistical recognition systems
handling multiple signers,” Computer Vision and Image Understanding, vol. 141, pp. 108–125, 2015.
4. R. E. Mitchell, T. A. Young, B. BACHELDA, and M. A. Karchmer, “How many people use asl in the united states? why estimates need
updating,” Sign Language Studies, vol. 6, no. 3, pp. 306–335, 2006.
5. D. Bragg, O. Koller, M. Bellard, L. Berke, P. Boudrealt, A. Braffort, N. Caselli, M. Huenerfauth, H. Kacorri, T. Verhoef et al.,
“Sign language recognition, generation, and translation: An interdisciplinary perspective,” arXiv preprint arXiv:1908.08597,
2019.
6. G. T. Papadopoulos and P. Daras, “Human action recognition using 3d reconstruction data,” IEEE Transactions on Circuits and
Systems for Video Technology, vol. 28, no. 8, pp. 1807–1823, 2016.
7. C. Padden, “Verbs and role-shifting in american sign language,” in Proceedings of the fourth national symposium on sign language
research and teaching, vol.National Association of the Deaf Silver Spring, MD, 1986, p. 57.
8. K. Emmorey, “Space on hand: The exploitation of signing space to illustrate abstract thought.” 2001.
9. H. Cooper, B. Holt, and R. Bowden, “Sign language recognition,” in Visual Analysis of Humans. Springer, 2011, pp. 539–562.
10. F. Ronchetti, F. Quiroga, C. A. Estrebou, L. C. Lanzarini, and A. Rosete, “Lsa64: an argentinian sign language dataset,” in XXII Congreso
Argentino de Ciencias de la Computacion (CACIC 2016). ´ , 2016.
11. M. W. Kadous et al., “Machine recognition of auslan signs using powergloves: Towards large-lexicon recognition of sign language,”
in Proceedings of the Workshop on the Integration of Gesture in Language and Speech, vol. 165, 1996.
12. C. Wang, Z. Liu, and S.-C. Chan, “Superpixel-based hand gesture recognition with kinect depth camera,” IEEE transactions on
multimedia, vol. 17, no. 1, pp. 29–39, 2014.
13. G. D. Evangelidis, G. Singh, and R. Horaud, “Continuous gesture recognition from articulated poses,” in European Conference on
Computer Vision. Springer, 2014, pp. 595–607.
14. J. Zhang, W. Zhou, C. Xie, J. Pu, and H. Li, “Chinese sign language recognition with adaptive hmm,” in 2016 IEEE International
Conference on Multimedia and Expo (ICME). IEEE, 2016, pp. 1–6.
15. Jungpil shin yuto akiba, koki hirooka 1,(member, ieee)(graduate student member, ieee), najmul hassan (graduate student member, ieee),
and yong seok hwang