EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 53 (2023) ITM Web Conf., 53 (2023) 01003 References
Open Access
Issue
ITM Web Conf.
Volume 53, 2023
2nd International Conference on Data Science and Intelligent Applications (ICDSIA-2023)
Article Number 01003
Number of page(s) 10
Section Artificial Intelligence
DOI https://doi.org/10.1051/itmconf/20235301003
Published online 01 June 2023
  1. Li, Dongxu, et al. “Word-level deep sign language recognition from video: A new largescale dataset and methods comparison.” Proceedings of the IEEE/CVF winter conference on applications of computer vision. 2020. [Google Scholar]
  2. Dong, Cao, Ming C. Leu, and Zhaozheng Yin. “American sign language alphabet recognition using microsoft kinect.” Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 2015. [Google Scholar]
  3. Ko, Sang-Ki, et al. “Neural sign language translation based on human keypoint estimation.” Applied Sciences 9.13 (2019): 2683. [CrossRef] [Google Scholar]
  4. De Coster, Mathieu, Mieke Van Herreweghe, and Joni Dambre. “Sign language recognition with transformer networks.” 12th International Conference on Language Resources and Evaluation. European Language Resources Association (ELRA), 2020. [Google Scholar]
  5. Cao, Zhe, et al. “Realtime multi-person 2d pose estimation using part affinity fields.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. [Google Scholar]
  6. Pigou, Lionel, Mieke Van Herreweghe, and Joni Dambre. “Sign classification in sign language corpora with deep neural networks.” International Conference on Language Resources and Evaluation (LREC), Workshop, Proceedings. 2016. [Google Scholar]
  7. Koller, Oscar, Sepehr Zargaran, and Hermann Ney. “Re-sign: Re-aligned end-to-end sequence modelling with deep recurrent CNN-HMMs.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017. [Google Scholar]
  8. Hossen, M. A., et al. “Bengali sign language recognition using deep convolutional neural networks.” 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR). IEEE, 2018. [Google Scholar]
  9. Rafi, Abdul Muntakim, et al. “Image-based Bengali Sign Language Alphabet Recognition for Deaf and Dumb Community.” 2019 IEEE Global Humanitarian Technology Conference (GHTC). IEEE, 2019. [Google Scholar]
  10. Hasan, Md Mehedi, Azmain Yakin Srizon, and Md Al Mehedi Hasan. “Classification of Bengali Sign Language Characters by Applying a Novel Deep Convolutional Neural Network.” 2020 IEEE Region 10 Symposium (TENSYMP). IEEE, 2020. [Google Scholar]
  11. Chollet, F., & others. (2015). Keras. GitHub. Retrieved from https://github.com/fchollet/keras [Google Scholar]
  12. Simonyan, Karen, and Andrew Zisserman. “Very deep convolutional networks for largescale image recognition.” arXiv preprint arXiv:1409.1556 (2014). [Google Scholar]
  13. He, Kaiming, et al. “Deep residual learning for image recognition.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. [Google Scholar]
  14. Szegedy, Christian, et al. “Rethinking the inception architecture for computer vision.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016 [Google Scholar]
  15. He, Kaiming, et al. “Identity mappings in deep residual networks.” European conference on computer vision. Springer, Cham, 2016. [Google Scholar]
  16. Chollet, François. “Xception: Deep learning with depthwise separable convolutions.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. [Google Scholar]
  17. Huang, Gao, et al. “Densely connected convolutional networks.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. [Google Scholar]
  18. Szegedy, Christian, et al. “Inception-v4, inception-resnet and the impact of residual connections on learning.” Thirty-first AAAI conference on artificial intelligence. 2017. [Google Scholar]
  19. Zoph, Barret, et al. “Learning transferable architectures for scalable image recognition.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. [Google Scholar]
  20. B. Zoph and Q. V. Le. Neural architecture search with reinforcement learning. In International Conference on Learning Representations, 2017. [Google Scholar]
  21. Rossum, G and F L. Drake. “Python 3 Reference Manual.” (2009). [Google Scholar]
  22. Bisong, Ekaba. Building machine learning and deep learning models on Google cloud platform. Berkeley, CA, USA: Apress, 2019. [CrossRef] [Google Scholar]
  23. Deng, J, W Dong, R Socher, K Li and L Fei-Fei. “Imagenet: A large-scale hierarchical image database.” (2009). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.