Building Sign Language Datasets

1. El Ghoul, O.E.G., Othman, A.O., Aziz, M.A., Sedrati, S.S.: JUMLA-QSL-22: A dataset of Qatari sign language sentences, https://ieee-dataport.org/open-access/jumla-qsl-22-dataset-qatari-sign-language-sentences. https://doi.org/10.21227/CKZP-3754.

2. Othman, A., El Ghoul, O., Aziz, M., Chemnad, K., Sedrati, S., Dhouib, A.: JUMLA-QSL-22: Creation and Annotation of a Qatari Sign Language Corpus for Sign Language Processing. In: Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments. pp. 686–692 (2023).

   Google Scholar 

3. Othman, A., El Ghoul, O.: BuHamad - The first Qatari virtual interpreter for Qatari Sign Language. NAFATH. 7, (2022). https://doi.org/10.54455/MCN.20.01.

4. Shi, B., Brentari, D., Shakhnarovich, G., Livescu, K.: Open-Domain Sign Language Translation Learned from Online Video, http://arxiv.org/abs/2205.12870, (2022).

   Google Scholar 

5. Neidle, C., Thangali, A., Sclaroff, S.: Challenges in development of the American Sign Language Lexicon Video Dataset (ASLLVD) corpus. Presented at the (2012).

   Google Scholar 

6. Sams, A., Akash, A.H., Rahman, S.M.M.: SignBD-Word: Video-Based Bangla Word-Level Sign Language and Pose Translation. In: 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT). pp. 1–7. IEEE, Delhi, India (2023). https://doi.org/10.1109/ICCCNT56998.2023.10306914.

7. Neidle, C., Opoku, A., Metaxas, D.: ASL Video Corpora & Sign Bank: Resources Available through the American Sign Language Linguistic Research Project (ASLLRP), http://arxiv.org/abs/2201.07899, (2022).

8. Li, D., Opazo, C.R., Yu, X., Li, H.: Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison. In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 1448–1458. IEEE, Snowmass Village, CO, USA (2020). https://doi.org/10.1109/WACV45572.2020.9093512.

9. Adithya, V., Rajesh, R.: Hand gestures for emergency situations: A video dataset based on words from Indian sign language. Data in Brief. 31, 106016 (2020). https://doi.org/10.1016/j.dib.2020.106016.

   Article  Google Scholar 

10. Bohacek, M., Hruz, M.: Learning from What is Already Out There: Few-shot Sign Language Recognition with Online Dictionaries. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG). pp. 1–6. IEEE, Waikoloa Beach, HI, USA (2023). https://doi.org/10.1109/FG57933.2023.10042544.

11. Cooper, H., Pugeault, N., Bowden, R.: Reading the signs: A video based sign dictionary. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops). pp. 914–919. IEEE, Barcelona, Spain (2011). https://doi.org/10.1109/ICCVW.2011.6130349.

12. Jedlička, P., Krňoul, Z., Železný, M., Muller, L.: MC-TRISLAN: A Large 3D Motion Capture Sign Language Data-set. Presented at the SIGNLANG (2022).

    Google Scholar 

13. Kiran Kumar, E., Kishore, P.V.V., Sastry, A.S.C.S., Anil Kumar, D.: 3D Motion Capture for Indian Sign Language Recognition (SLR). In: Satapathy, S.C., Bhateja, V., and Das, S. (eds.) Smart Computing and Informatics. pp. 21–29. Springer Singapore, Singapore (2018). https://doi.org/10.1007/978-981-10-5547-8_3.

    Chapter  Google Scholar 

14. Watanabe, K., Nagashima, Y., Hara, D., Horiuchi, Y., Sako, S., Ichikawa, A.: Construction of a Japanese Sign Language Database with Various Data Types. In: Stephanidis, C. (ed.) HCI International 2019 - Posters. pp. 317–322. Springer International Publishing, Cham (2019). https://doi.org/10.1007/978-3-030-23522-2_41.

15. Benchiheub, M.-E.-F., Berret, B., Braffort, A.: Collecting and Analysing a Motion-Capture Corpus of French Sign Language. Presented at the (2016).

    Google Scholar 

16. Dibeklioglu, H., Dikici, E., Santemiz, P., Balci, K., Akarun, L.: Sign Language Motion Tracking and Generating 3D Motion Pieces Using 2D Features. In: 2007 IEEE 15th Signal Processing and Communications Applications. pp. 1–4. IEEE, Eskisehir, Turkey (2007). https://doi.org/10.1109/SIU.2007.4298843.

17. Kiran, P.S., Kumar, D.A., Kishore, P.V.V., Kumar, E.K., Kumar, M.T.K., Sastry, A.S.C.S.: Investigation of 3-D Relational Geometric Features for Kernel-Based 3-D Sign Language Recognition. In: 2019 IEEE International Conference on Intelligent Systems and Green Technology (ICISGT). pp. 31–313. IEEE, Visakhapatnam, India (2019). https://doi.org/10.1109/ICISGT44072.2019.00022.

18. Brock, H., Law, F., Nakadai, K., Nagashima, Y.: Learning Three-dimensional Skeleton Data from Sign Language Video. ACM Trans. Intell. Syst. Technol. 11, 1–24 (2020). https://doi.org/10.1145/3377552.

    Article  Google Scholar 

19. Ben Haj Amor, A., El Ghoul, O., Jemni, M.: An EMG dataset for Arabic sign language alphabet letters and numbers. Data in Brief. 51, 109770 (2023). https://doi.org/10.1016/j.dib.2023.109770.

20. Duarte, A., Palaskar, S., Ventura, L., Ghadiyaram, D., DeHaan, K., Metze, F., Torres, J., Giro-i-Nieto, X.: How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language, http://arxiv.org/abs/2008.08143, (2021).

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