A Survey on Different Approach Used for Sign Language Recognition Using Machine Learning
DOI:
https://doi.org/10.51983/ajcst-2023.12.1.3554Keywords:
Sign Language, ANN, CNN, SVM, HMMAbstract
This paper presents a survey of different approaches used for sign language recognition using machine learning. Sign language recognition is a challenging problem that has attracted considerable research attention in recent years. Various machine learning techniques such as artificial neural networks, support vector machines, decision trees, and convolutional neural networks have been explored to recognize sign language gestures. The survey discusses the strengths and limitations of each approach, as well as their performance on different datasets. Moreover, it also discusses some recent advancements in sign language recognition, including the use of depth sensors and wearable devices. The survey concludes that while machine learning approaches have shown promising results, there is still room for improvement in terms of recognition accuracy, robustness to variations, and real-time performance.
References
R. K. Tripathy and S. K. Senapati, "Indian sign language recognition using contour features and Hu moments," International Conference on Pattern Recognition and Machine Intelligence, 2017.
D. Koller, P. Maniatis, and M. Konečný, "Deep learning for sign language recognition," International Conference on Automatic Face and Gesture Recognition, 2017.
L. B. P. de Carvalho, D. A. V. da Silva, and A. M. da Costa, "LIBRAS recognition using deep learning," International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, 2019.
S. Mohamad, M. Rizon, and M. Ahmad, "Hybrid approach for Malaysian sign language recognition using convolutional neural network and handcrafted features," International Journal of Computer Science and Information Security, vol. 17, no. 7, 2019.
Y. Haddou, S. Satori, and S. E. El Alaoui, "Hybrid approach for Moroccan sign language recognition," International Conference on Image and Signal Processing, 2019.
A. F. Özcan and S. Sariyanidi, "Recognition of Turkish Sign Language alphabet using sensor data," Journal of Intelligent and Fuzzy Systems, vol. 32, no. 1, 2017.
S. Karaoglu, S. Sariyanidi, and A. Ercil, "Turkish sign language recognition with transfer learning," International Conference on Image Analysis and Processing, 2019.
N. K. Macharia and S. K. Kimwele, "Transfer learning for sign language recognition using deep convolutional neural networks," International Conference on Computing and Data Science, 2018.
A. Basharat, T. Xiang, and S. Gong, "Sign language recognition using sub-units based on discriminative random fields," International Conference on Computer Vision, 2009.
S. Prasanna and S. Selvakumar, "Dynamic time warping-based approach for Indian sign language recognition," International Conference on Information Communication and Embedded Systems, 2014.
J. Pu, M. Zeng, and J. Li, "Spatial-temporal modeling for sign language recognition," International Conference on Computer Vision, 2017.
M. Wachs, B. Karp, and T. Stern, "Multi-modal American Sign Language recognition using video and accelerometer data," International Conference on Intelligent User Interfaces, 2011.
G. Can, O. Sen, and A. Ercil, "Turkish sign language recognition with ensemble of CNN and RNN," Signal Processing and Communications Applications Conference, 2020.
M. I. Polat, M. Kaya, and A. Ercil, "One-shot Turkish sign language recognition with few-shot learning," Signal Processing and Communications Applications Conference, 2021.
Y. Zhang, W. Wu, and M. Xu, "Attention-based Chinese sign language recognition using convolutional and recurrent neural networks," IEEE Access, 2020.
S. S. Prabhakar and B. G. Kumar, "Sign Language Recognition using Artificial Neural Networks," International Journal of Computer Science and Mobile Computing, vol. 5, no. 1, pp. 121-127, January 2016.
N. Akhtar, M. Akram, and M. Awais, "Sign Language Recognition using Support Vector Machines," in Proceedings of the 2017 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp. 1-5, March 2017.
S. K. Sharma and K. K. Chhabra, "Sign Language Recognition using Decision Trees and Adaptive Boosting," in Proceedings of the 2017 IEEE International Conference on Signal Processing, Computing and Control (ISPCC), pp. 388-392, September 2017.
H. B. L. D. Silva and G. M. Dias, "Sign Language Recognition using Convolutional Neural Networks," in Proceedings of the 2019 IEEE International Symposium on Multimedia (ISM), pp. 99-104, December 2019.
M. H. Iqbal and M. Asad-Ur-Rehman, "Sign Language Recognition using Depth Sensors: A Review," Journal of King Saud University - Computer and Information Sciences, vol. 32, no. 3, pp. 244-252, July 2020.
M. Khan, S. Raza, and M. A. Yousaf, "Real-Time Sign Language Recognition using Wearable Devices: A Review," Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 2, pp. 1447-1465, February 2021.
Z. Ye, Y. Yu, and J. Wang, "Sign Language Recognition Using Convolutional Neural Networks," in Proceedings of the 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO), December 2017.
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