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NTIS 바로가기Scientific reports, v.12, 2022년, pp.11964 -
Subramanian, Barathi (School of Computer Science and Engineering, Kyungpook National University, Buk-gu, Daegu, 41566 South Korea) , Olimov, Bekhzod (School of Computer Science and Engineering, Kyungpook National University, Buk-gu, Daegu, 41566 South Korea) , Naik, Shraddha M. (School of Computer Science and Engineering, Kyungpook National University, Buk-gu, Daegu, 41566 South Korea) , Kim, Sangchul (Division of Computer Engineering, Hankuk University of Foreign Studies, Seoul, South Korea) , Park, Kil-Houm (School of Electronics Engineering, Kyungpook National University, Buk-gu, Daegu, 41566 South Korea) , Kim, Jeonghong (School of Computer Science and Engineering, Kyungpook National University, Buk-gu, Daegu, 41566 South Korea)
Sign language recognition is challenged by problems, such as accurate tracking of hand gestures, occlusion of hands, and high computational cost. Recently, it has benefited from advancements in deep learning techniques. However, these larger complex approaches cannot manage long-term sequential data...
1. Jain RK Rathi SK Mathur R Gupta CP Katewa V Jat DS Yadav N A review paper on sign language recognition using machine learning techniques Emerging Trends in Data Driven Computing and Communications 2021 Springer
2. Aloysius N Geetha M Nedungadi P Incorporating relative position information in transformer-based sign language recognition and translation IEEE Access 2021 9 145929 145942 10.1109/ACCESS.2021.3122921
3. Li, D., Rodríguez, C., Yu, X. & Li, H. Word-level deep sign language recognition from video: A new large-scale dataset and methods comparison. arXiv:1910.11006v2 (2019).
5. Wadhawan A Kumar P Deep learning-based sign language recognition system for static signs Neural Comput. Appl. 2020 32 7957 7968 10.1007/s00521-019-04691-y
6. Zafrulla, Z., Brashear, H., Starner, T., Hamilton, H. & Presti, P. American sign language recognition with the kinect. In Proceedings of the 13th International Conference on Multimodal Interfaces (ICMI ’11) 279–286 (Association for Computing Machinery, 2011). 10.1145/2070481.2070532.
7. Kumar P Gauba H Roy PP Dogra DP Coupled HMM-based multi-sensor data fusion for sign language recognition Pattern Recognit. Lett. 2017 86 C 1 8 10.1016/j.patrec.2016.12.004
8. Kumar P Gauba H Roy PP Dogra DP A multimodal framework for sensor based sign language recognition Neurocomputing 2017 259 21 38 10.1016/j.neucom.2016.08.132
9. Elakkiya R Selvamani K Subunit sign modeling framework for continuous sign language recognition Comput. Electr. Eng. 2019 74 379 390 10.1016/j.compeleceng
10. Gadekallu TR Hand gesture classification using a novel CNN-crow search algorithm Complex Intell. Syst. 2021 7 1855 1868 10.1007/s40747-021-00324-x
11. Ibrahim NB Zayed H Selim M Advances, challenges and opportunities in continuous sign language recognition J. Eng. Appl. Sci. 2019 15 5 1205 1227 10.36478/jeasci.2020.1205.1227
12. Koller, O. Quantitative survey of the state of the art in sign language recognition. arXiv (2020).
13. Mittal A Kumar P Roy PP Balasubramanian R Chaudhuri BB A modified LSTM model for continuous sign language recognition using leap motion IEEE Sens. J. 2019 19 7056 7063 10.1109/JSEN.2019.2909837
14. Kanisha B Smart communication using tri-spectral sign recognition for hearing-impaired people J. Supercomput. 2022 78 2651 2664 10.1007/s11227-021-03968-1
15. Sun Z Bhatia SK A survey on dynamic sign language recognition Advances in Computer, Communication and Computational Sciences 2021 Springer
16. Rakesh S Bharadhwaj A Sree HE Raj JS Sign language recognition using convolutional neural network Innovative Data Communication Technologies and Application 2021 Springer
17. Kiran Kumar E Kishore PVV Sastry ASCS Anil Kumar D Satapathy S Bhateja V Das S 3D motion capture for Indian sign language recognition (SLR) Smart Computing and Informatics 2018 Springer
18. Itkarkar Rajeshri R Nandi AKV Mungurwadi VB Mukherjee M Indian sign language recognition using combined feature extraction Advances in Medical Physics and Healthcare Engineering 2021 Springer
19. Starner T Weaver J Pentland A Real-time American sign language recognition using desk and wearable computer based video IEEE Trans. Pattern Anal. Mach. Intell. 1999 20 1371 1375 10.1109/34.735811
20. Vogler, C. & Metaxas, D. N. Adapting hidden Markov models for ASL recognition by using three-dimensional computer vision methods. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics Vol. 1 (1970). 10.1109/ICSMC.1997.625741.
21. Shukor AZ Miskon MF Jamaluddin MH Ali FB Asyraf MF Bahar MB A new data glove approach for Malaysian sign language detection Procedia Comput. Sci. 2015 76 60 67 10.1016/j.procs.2015.12.276
22. Almeida S Guimaraes FG Ramirez JA Feature extraction in Brazilian sign language recognition based on phonological structure and using RGB-D sensors Expert Syst. Appl. 2014 41 16 7259 7271 10.1016/j.eswa.2014.05.024
23. Patil A Kulkarni A Yesane H Sadani M Satav P Sharma N Literature survey: Sign language recognition using gesture recognition and natural language processing Data Management, Analytics and Innovation 2021 Springer
24. Hurroo M Elham M Sign language recognition system using convolutional neural network and computer vision Int. J. Eng. Res. Technol. (IJERT) 2020 9 12 59 64
25. Rastgoo R Kiani K Escalera S Hand sign language recognition using multi-view hand skeleton Expert Syst. Appl. 2020 150 113336 10.1016/j.eswa.2020.113336
26. Lee CKM Ng KKH Chen CH Lau HCW Chung SY Tsoi T American sign language recognition and training method with recurrent neural network Expert Syst. Appl. 2021 167 October 114403 10.1016/j.eswa.2020.114403
27. Chen R-C Dewi C Huang S-W Caraka RE Selecting critical features for data classification based on machine learning methods J. Big Data 2020 7 52 10.1186/s40537-020-00327-4
28. Gupta R Kumar A Indian sign language recognition using wearable sensors and multi-label classification Comput. Electr. Eng. 2020 90 December 106898 10.1016/j.compeleceng.2020.106898
29. Grishchenko, I. & Bazarevsky, V. Mediapipe holistic. Retrieved from https://ai.googleblog.com/2020/2012 20/ (2020).
30. Naglot, D. & Kulkarni, M. Recognition using the leap motion controller. In International Conference on Inventive Computation Technologies (ICICT) Vol. 2, 1–6 (2016). 10.1109/INVENTIVE.2016.7830097.
31. Bhagat, N. K., Vishnusai, Y. & Rathna, G. N. Indian sign language gesture recognition using image processing and deep learning. In 2019 Digital Image Computing: Techniques and Applications (DICTA) (2019). 10.1109/DICTA47822.2019.8945850
32. Raghuveera Tripuraribhatla R Deepthi R Mangalashri R Akshaya R A depth-based Indian Sign language recognition using Microsoft Kinect Sadhana Acad. Proc. Eng. Sci. 2020 45 1 1 13 10.1007/s12046-019-1250-6
33. Neethu PS Ramadass S Sathish D An efficient method for human hand gesture detection and recognition using deep learning convolutional neural networks Soft Comput. 2020 24 20 15239 15248 10.1007/s00500-020-04860-5
34. Salem N Alharbi S Khezendar R Alshami H Real-time glove and android application for visual and audible Arabic sign language translation Procedia Comput. Sci. 2019 163 450 459 10.1016/j.procs.2019.12.128
35. Rastgoo R Kiani K Escalera S Real-time isolated hand sign language recognition using deep networks and SVD J. Ambient Intell. Humaniz. Comput. 2021 10.1007/s12652-021-02920-8
36. Rastgoo R Kiani K Escalera S Hand pose aware multimodal isolated sign language recognition Multimed Tools Appl. 2021 80 127 163 10.1007/s11042-020-09700-0
37. Rastgoo R Kiani K Escalera S Video-based isolated hand sign language recognition using a deep cascaded model Multimedia Tools Appl. 2020 79 31–32 22965 22987 10.1007/s11042-020-09048-5
38. Al-Hammadi M Hand gesture recognition for sign language using 3DCNN IEEE Access 2020 8 79491 79509 10.1109/ACCESS.2020.2990434
39. Chen C Liu L Wan S Hui X Pei Q Data dissemination for industry 4.0 applications in internet of vehicles based on short-term traffic prediction ACM Trans. Internet Technol. 2022 22 1 18 10.1145/3430505
40. Carreira, J. & Zisserman, A. (2017) Quo vadis action recognition? A new model and the kinetics dataset. In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 6299–6308 (2017).
41. Quiroga, F., Ronchetti, F., Estrebou, C. A., Lanzarini, L. C. & Rosete, A. Lsa64: An argentinian sign language dataset. In XXII Congreso Argentino de Ciencias de la Computación 794–803 (CACIC, 2016).
42. Ojha A Pandey A Maurya S Thakur A Dayananda P Sign language to text and speech translation in real time using convolutional neural network Int. J. Eng. Res. Technol. (IJERT) 2020 8 15 191 196
43. Koller, O., Ney, H. & Bowden, R. Deep hand: How to train a CNN on 1 million hand images when your data is continuous and weakly labelled. In IEEE International Conference on Computer Vision and Pattern Recognition Vol. 2016-Decem, 3793–3802 (2016). 10.1109/CVPR.2016.412
44. Mocialov, B., Turner, G. H., Lohan, K. S. & Hastie, H. Towards continuous sign language recognition with deep learning. In Proceedings of the Workshop on the Creating Meaning With Robot Assistants: The Gap Left by Smart Devices (2017).
45. Molchanov, P., Yang, X., Gupta, S., Kim, K., Tyree, S. & Kautz, J. Online detection and classification of dynamic hand gestures with recurrent 3D convolutional neural networks. In IEEE International Conference on Computer Vision and Pattern Recognition vol. 2016-December, 4207–4215 (2016).
46. Elakkiya R Selvamani K Enhanced dynamic programming approach for subunit modelling to handle segmentation and recognition ambiguities in sign language J. Parallel Distrib. Comput. 2018 117 246 255 10.1016/j.jpdc.2017.07.001
47. Cheok MJ Omar Z Jaward MH A review of hand gesture and sign language recognition techniques Int. J. Mach. Learn. Cybern. 2019 10 1 131 153 10.1007/s13042-017-0705-5
48. Nai W Liu Y Rempel D Wang Y Fast hand posture classification using depth features extracted from random line segments Pattern Recognit. 2017 65 November 1 10 10.1016/j.patcog.2016.11.022
49. Elakkiya R Machine learning based sign language recognition: a review and its research frontier J. Ambient Intell. Humaniz. Comput. 2021 10.1007/s12652-020-02396-y
50. Adithya, V., Vinod, P. R. & Gopalakrishnan, U. Artificial neural network based method for Indian sign language recognition. In 2013 IEEE Conference on Information and Communication Technologies (ICT) 1080–1085 (2013). 10.1109/CICT.2013.6558259.
51. Meng, X. J., Qiu, S., Wan, S., Cheng, K. & Cui, L. A motor imagery EEG signal classification algorithm based on recurrence plot convolution neural network. Pattern Recognit. Lett. 134146 , 134–141. 10.1016/j.patrec.2021.03.023 (2021). ISSN 0167-8655.
52. Xiao L Fan C Ouyang H Abate AF Wan S Adaptive trapezoid region intercept histogram based Otsu method for brain MR image segmentation J. Ambient Intell. Hum. Comput. 2022 13 2161 2176 10.1007/s12652-021-02976-6
53. Lyu, Y. & Huang, X. Road segmentation using CNN with GRU. Comput. Vis. Pattern Recognit. arXiv:1804.05164 (2018).
54. Cho, K., Van Merriënboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder–decoder approaches. Comput. Lang. arxiv:1409.1259 (2014).
55. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H. & Bengio, Y. Learning phrase representations using RNN encoder–decoder for statistical machine translation. Comput. Lang. Retrieved from arxiv:1406.1078 (2014).
56. Olimov B Weight initialization based-rectified linear unit activation function to improve the performance of a convolutional neural network model Pract. Exp. Concurr. Comput. 2020 10.1002/cpe.6143
57. Olimov B Kim J Paul A Deep clean before training network: Training deep convolutional neural networks with extremely noisy labels IEEE Access 2020 8 220482 220495 10.1109/ACCESS.2020.3041873
58. Olimov B Kim J Paul A REF-Net: Robust, efficient, and fast network for semantic segmentation applications using devices with limited computational resources IEEE Access 2021 9 15084 15098 10.1109/ACCESS.2021.3052791
59. Gulcehre, C., Moczulski, M., Denil, M. & Bengio, Y. Noisy activation functions. In Proceedings of the 33rd International Conference on International Conference on Machine Learning, (ICML’16) Vol. 48. JMLR.org, 3059–3068 (2016).
60. Ravanelli M Brakel P Omologo M Bengio Y Light gated recurrent units for speech recognition IEEE Trans. Emerg. Top. Comput. 2018 2 2 92 102 10.1109/TETCI.2017.2762739
61. Clevert, D.A., Unterthiner, T. & Hochreiter, S. Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUS). arXiv:1511.07289v5 (2015).
62. Pascanu, R., Mikolov, T. & Bengio, Y. On the difficulty of training recurrent neural networks. In Proceedings of the 30th International Conference on International Conference on Machine Learning, (ICML’13) Vol. 28. JMLR.org, III-1310–III-1318 (2013).
63. Li, D., Rodriguez, C., Yu, X. & Li, H. Word-level deep sign language recognition from video: A new large-scale dataset and methods comparison. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision 1459–1469 (2020).
64. Kingma, D. P. & Ba, J. Adam: A method for stochastic optimization. arxiv:1412.6980 (2014).
65. Ullah A Ahmad J Muhammad K Sajjad M Baik SW Action recognition in video sequences using deep bi-directional LSTM with CNN features IEEE Access 2017 6 1155 1166 10.1109/ACCESS.2017.2778011
66. Olimov B FU-Net: Fast biomedical image segmentation model based on bottleneck convolution layers Multimedia Syst. 2021 27 1 14 10.1007/s00530-020-00726-w
67. Olimov B Koh S-J Kim J AEDCN-Net: Accurate and efficient deep convolutional neural network model for medical image segmentation IEEE Access 2021 10.1109/ACCESS.2021.3128607
68. Olimov, B., Kim, J., Paul, A. & Subramanian, B. An efficient deep convolutional neural network for semantic segmentation. In 8th International Conference on Orange Technology (ICOT) 1–9 (2020). 10.1109/ICOT51877.2020.9468748.
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