Recent Progress in Arabic Sign Language Recognition: Utilizing Convolutional Neural Networks (CNN)

Open Access

Issue		BIO Web Conf. Volume 97, 2024 Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)


Article Number		00073
Number of page(s)		16
DOI		https://doi.org/10.1051/bioconf/20249700073
Published online		05 April 2024

P. Neto, M. Simão, N. Mendes and M. Safeea, “Gesture-based human-robot interaction for human assistance in manufacturing,” The International Journal of Advanced Manufacturing Technology, vol. 101, no. 1, pp. 119–135, 2019. https://doi.org/10.1007/s00170-018-2788-x [CrossRef] [Google Scholar]
T. Kamnardsiri, L. O. Hongsit, P. Khuwuthyakorn and N. Wongta, “The effectiveness of the game-based learning system for the improvement of American sign language using Kinect,” Electronic Journal of eLearning, vol. 15, no. 4, pp. 283–296, 2017. [Google Scholar]
A. Vaitkevičius, M. Taroza, T. Blažauskas, R. Damaševičius, R. Maskeliūnas et al., “Recognition of American sign language gestures in virtual reality using Leap Motion,” Applied Sciences, 9, no. 3, pp. 445, 2019. [CrossRef] [Google Scholar]
https://doi.org/10.3390/app9030445 [Google Scholar]
M. A. Rahim, M. R. Islam and J. Shin, “Non-touch sign word recognition based on dynamic hand gesture using hybrid segmentation and CNN feature fusion,” Applied Sciences, 9, no. 18, pp. 3790, 2019. https://doi.org/10.3390/app9183790 [CrossRef] [Google Scholar]
M. J. Cheok, Z. Omar and M. H. Jaward, “A review of hand gesture and sign language recognition techniques,” International Journal of Machine Learning and Cybernetics, vol. 10, no. 1, pp. 131–153, 2019. [CrossRef] [Google Scholar]
https://doi.org/10.1007/s13042-017-0705-5 [Google Scholar]
M. Al-Hammadi, G. Muhammad, W. Abdul, M. Alsulaiman, M. A. Bencherif et al., “Hand gesture recognition for sign language using 3DCNN,” IEEE Access, vol. 8, no. 79, pp. 491–509, 2020. DOI: 10.1109/ACCESS.2020.2990434 [Google Scholar]
M. Jebali, A. Dakhli and M. Jemni, “Vision-based continuous sign language recognition using multimodal sensor fusion,” Evolving Systems, Vol. 12, pp. 1031–1044, 2021. [CrossRef] [Google Scholar]
https://doi.org/10.1007/s12530-020-09365-y [Google Scholar]
R. Elakkiya, “Machine learning-based sign language recognition: A review and its research frontier,” Journal of Ambient Intelligence and Humanized Computing, Vol. 12, pp. 7205–7224, 2021. [CrossRef] [Google Scholar]
https://doi.org/10.1007/s12652-020-02396-y [Google Scholar]
K. Kudrinko, E. Flavin, X. Zhu and Q. Li, “Wearable sensor-based sign language recognition: A comprehensive review,” in IEEE Reviews in Biomedical Engineering, Vol. 14, pp. 82–97, 2021. [CrossRef] [PubMed] [Google Scholar]
DOI: 10.1109/RBME.2020.3019769 [Google Scholar]
M. A. Rahim, A. S. M. Miah, A. Sayeed and J. Shin, “Hand gesture recognition based on optimal segmentation in human-computer interaction,” in Proc. of the 3rd IEEE Int. Conf. on Knowledge Innovation and Invention (ICKII), Taiwan, pp. 163–166, 2020. [CrossRef] [Google Scholar]
DOI: 10.1109/ICKII50300.2020.9318870 [Google Scholar]
R. Kushalnagar, “Deafness and hearing loss,” Web Accessibility, Springer, Berlin, Germany, pp. 35–47, 2019. https://doi.org/10.1007/978-1-4471-7440-0_3 [CrossRef] [Google Scholar]
N. M. Adaloglou, T. Chatzis, I. Papastratis, A. Stergioulas, G. T. Papadopoulos et al., “A comprehensive study on deep learning-based methods for sign language recognition,” IEEE Transactions on Multimedia, Vol. 24, pp. 1–1, 2021. DOI: 10.1109/TMM.2021.3070438 [Google Scholar]
S. Zeng, B. Zhang B.J. Gou and Y. Xu, “Regularization on augmented data to diversify sparse representation for robust image classification,” IEEE Transactions on Cybernetics, pp. 1–14, 2020. https://dx.doi.org/10.1109/TCYB.2020.3025757 [Google Scholar]
R. Thilahar and R. Sivaramakrishnan, “Fuzzy neuro-genetic approach for feature selection and image classification in augmented reality systems,” International Journal of Robotics and Automation (IJRA), vol. 8, no. 3, pp. 194–204, 2019. [CrossRef] [Google Scholar]
G. Latif, N. Mohammad, R. AlKhalaf, R. AlKhalaf, J. Alghazo et al. “An automatic Arabic sign language recognition system based on deep CNN: An assistive system for the deaf and hard of hearing,” International Journal of Computing and Digital Systems, vol. 9, no. 4, pp. 715–724, 2020. http://dx.doi.org/10.12785/ijcds/090418 [CrossRef] [Google Scholar]
A. Ahmed, R. A. Alez, G. Tharwat, M. Taha, B. Belgacem et al. “Arabic sign language intelligent translator,” The Imaging Science Journal, vol. 68, no. 1, pp. 11–23, 2020. [CrossRef] [Google Scholar]
https://doi.org/10.1080/13682199.2020.1724438 [Google Scholar]
A. S. Al-Shamayleh, R. Ahmad, N. Jomhari and M. A. Abushariah, “Automatic Arabic sign language recognition: A review, taxonomy, open challenges, research roadmap and future directions,” Malaysian Journal of Computer Science, vol. 33, no. 4, pp. 306–343, 2020. https://doi.org/10.22452/mjcs.vol33no4.5 [CrossRef] [Google Scholar]
S. M. Elatawy, D. M. Hawa, A. A. Ewees and A. M. Saad, “Recognition system for alphabet Arabic sign language using neutrosophic and fuzzy c-means,” Education and Information Technologies, vol. 25, no. 6, pp. 5601–5616, 2020. [CrossRef] [Google Scholar]
https://doi.org/10.1007/s10639-020-10184-6 [Google Scholar]
A. A. Samie, F. Elmisery, A. M. Brisha and A. Khalil, “Arabic sign language recognition using Kinect sensor,” Research Journal of Applied Sciences, Engineering and Technology, vol. 15, no. 2, pp. 57–67, 2018. https://doi.org/10.19026/rjaset.15.5292 [CrossRef] [Google Scholar]
Ahmed, A. A., and S. Aly (2014) Explored appearance-based ArSL recognition using Hidden Markov Models. Presented at the International Conference on Engineering and Technology (ICET). DOI: 10.1109/ICEngTechnol.2014.7016804 [Google Scholar]
Fathy, G. D., E. Emary, and H. N. ElMahdy (2015) Focused on supporting ArSL recognition with facial expressions. Featured in the Proceedings of the 7th International Conference on Information Technology (ICIT). [Google Scholar]
Ahmed, A. M., R. A. Alez, M. Taha, and G. Tharwat (2016) Developed a system for automatic translation of Arabic sign to Arabic text (ATASAT). Published in the Journal of Computer Science and Information Technology. DOI: 10.5121/csit.2016.60511 [Google Scholar]
Hayani, S., M. Benaddy, O. El Meslouhi, & M. Kardouchi (2019) Presented research on Arab sign language recognition using convolutional neural networks at the International Conference of Computer Science and Renewable Energies (ICCSRE). DOI: 10.1109/ICCSRE.2019.8807586 [Google Scholar]
Shahin, A. I., and S. Almotairi (2019) Worked on an automated Arabic Sign Language Recognition System based on Deep Transfer Learning. Published in the International Journal of Computer Science and Network Security. [Google Scholar]
Althagafi A., G. Althobaiti, T. Alsubait, and T. Alqurashi (2020) Investigated ASLR using Convolutional Neural Networks. Their work appeared in the International Journal of Computer Science and Network Security. [Google Scholar]
Elsayed, E. K., and D. R. Fathy (2020) Developed a sign language semantic translation system using ontology and deep learning. Published in the International Journal of Advanced Computer Science and Applications. DOI: 10.14569/ijacsa.2020.0110118 [Google Scholar]
Latif, G., N. Mohammad, R. AlKhalaf, R. AlKhalaf, J. Alghazo, and M. Khan (2020) Proposed an Automatic Arabic Sign Language Recognition System based on Deep CNN, aiming to assist the deaf and hard of hearing. Published in the International Journal of Computing and Digital Systems. http://dx.doi.org/10.12785/ijcds/090418 [Google Scholar]
Saleh, Y., and G. Issa (2020) Worked on Arabic Sign Language Recognition through deep neural networks fine-tuning. Their findings are documented in a detailed study. [Google Scholar]
Kamruzzaman, M.M. (2020) Focused on Arabic Sign Language Recognition and generating Arabic speech using a Convolutional Neural Network. This research was published in Wireless Communications and Mobile Computing. https://doi.org/10.1155/2020/3685614 [Google Scholar]
Latif, G., Mohammad, N., Alghazo, J., AlKhalaf, R., & AlKhalaf, R. (2019). ArASL: Arabic alphabets sign language dataset. Data in brief, 23, 103777. https://doi.org/10.1016/j.dib.2019.103777 [CrossRef] [PubMed] [Google Scholar]
Khudhair, Z. N., Nidhal, A., El Abbadi, N. K., Mohamed, F., Saba, T., Alamri, F. S., & Rehman, A. (2023). Color to Grayscale Image Conversion Based on Singular Value Decomposition. IEEE Access. DOI: 10.1109/ACCESS.2023.3279734 [Google Scholar]
Flusser, J., Farokhi, S., Höschl, C., Suk, T., Zitova, B., & Pedone, M. (2015). Recognition of images degraded by Gaussian blur. IEEE transactions on Image Processing, 25(2), 790–806. DOI: 10.1109/TIP.2015.2512108 [Google Scholar]
Dorothy, R., Joany, R. M., Rathish, R. J., Prabha, S. S., Rajendran, S., & Joseph, S. T. (2015). Image enhancement by histogram equalization. International Journal of Nano Corrosion Science and Engineering, 2(4), 21–30. [Google Scholar]
Maćkiewicz, A., & Ratajczak, W. (1993). Principal components analysis (PCA). Computers & Geosciences, 19(3), 303–342. https://doi.org/10.1016/0098-3004(93)90090-R [CrossRef] [Google Scholar]
Xanthopoulos, P., Pardalos, P. M., Trafalis, T. B., Xanthopoulos, P., Pardalos, P. M., & Trafalis, T. B. (2013). Linear discriminant analysis. Robust data mining, 27–33. https://doi.org/10.1007/978-1-4419-9878-1_4 [CrossRef] [Google Scholar]

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