Abstract
The target of SLR or sign language recognition is to interpret the sign language into text, respectively. So the deaf and mute people can communicate with ordinary people easily. Sign language recognition has a tremendous social impact; however, it is challenging due to the significant variations and complexity in the hand actions. There are many existing methods for recognizing sign language that uses handcraft features for describing the motion of sign language and then, based on the features it makes the classification models. To approach the problem, we have discussed considering the KNN that can conveniently extract the features. The proposed model can be validated on a real data set.
References
Ko BK, Yang HS. Finger mouse and gesture recognition system as a new human computer interface. Computers & Graphics, 1997; 21(5): 555-561. https://doi.org/10.1016/S0097-8493(97)00034-4
Pigou L. et al. Sign language recognition using convolutional neural networks. European Conference on Computer Vision. Springer, Cham, 2014.
Islam MR, Mitu UK, Bhuiyan RA, Shin J. Hand gesture feature extraction using deep convolutional neural network for recognizing American sign language. In 2018 4th International Conference on Frontiers of Signal Processing (ICFSP) 2018; pp. 115-119. IEEE. https://doi.org/10.1109/ICFSP.2018.8552044
Rao GA, Syamala K, Kishore PVV, Sastry ASCS. Deep convolutional neural networks for sign language recognition. In 2018 Conference on Signal Processing And Communication Engineering Systems (SPACES) 2018; pp. 194-197. IEEE. https://doi.org/10.1109/SPACES.2018.8316344
Rahaman MA, Jasim M, Ali MH, Hasanuzzaman M. Real-time computer vision-based Bengali sign language recognition. In 2014 17th international conference on computer and information technology (ICCIT) 2014; pp. 192-197. IEEE. https://doi.org/10.1109/ICCITechn.2014.7073150
Dahmani D, Larabi S. User-independent system for sign language finger spelling recognition. Journal of Visual Communication and Image Representation, 2014; 25(5): 1240-1250. https://doi.org/10.1016/j.jvcir.2013.12.019
Kalsh EA, Garewal NS. Sign language recognition system. International journal of computational engineering research, 2013; 3(6): 15-21.
Nandy A, Prasad JS, Mondal S, Chakraborty P, Nandi GC. Recognition of isolated indian sign language gesture in real time. In International Conference on Business Administration and Information Processing 2010; pp. 102-107. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12214-9_18
Galvão RKH, Yoneyama T. A competitive wavelet network for signal clustering. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2004; 34(2): 1282-1288. https://doi.org/10.1109/TSMCB.2003.817104
Parvathy P, Subramaniam K, Venkatesan GP, Karthikaikumar P, Varghese J, Jayasankar T. Development of hand gesture recognition system using machine learning. Journal of Ambient Intelligence and Humanized Computing, 2021; 12(6): 6793-6800. https://doi.org/10.1007/s12652-020-02314-2
Tsai TH, Huang CC, Zhang KL. Design of hand gesture recognition system for human-computer interaction. Multimedia Tools and Applications, 2020; 79(9): 5989-6007. https://doi.org/10.1007/s11042-019-08274-w
Bhuvaneshwari C, Manjunathan A. Advanced gesture recognition system using long-term recurrent convolution network. Materials Today: Proceedings, 2020; 21: 731-733. https://doi.org/10.1016/j.matpr.2019.06.748
Al-Hammadi M, Muhammad G, Abdul W, Alsulaiman M, Bencherif MA, Mekhtiche MA. Hand gesture recognition for sign language using 3DCNN. IEEE Access, 2020; 8: 79491-79509. https://doi.org/10.1109/ACCESS.2020.2990434
Rojasara D, Chitaliya N. Real time visual recognition of Indian sign language using wavelet transform and principle component analysis. International Journal of Soft Computing and Engineering (IJSCE), 2014; 4(3): 17-20.
Sharma S, Singh S. Vision-based hand gesture recognition using deep learning for the interpretation of sign language. Expert Systems with Applications, 2021; 182: 115657. https://doi.org/10.1016/j.eswa.2021.115657
Ghule S, Chavaan M. Implementation of Hand Gesture Recognition System to Aid Deaf-Dumb People. In Advances in Signal and Data Processing 2021; pp. 183-194. Springer, Singapore. https://doi.org/10.1007/978-981-15-8391-9_14
Tasmere D, Ahmed B, Das SR. Real time hand gesture recognition in depth image using cnn. International Journal of Computer Applications, 975: 8887.
Vanaja S, Preetha R, Sudha S. Hand Gesture Recognition for Deaf and Dumb Using CNN Technique. In 2021 6th International Conference on Communication and Electronics Systems (ICCES) 2021; pp. 1-4 IEEE. https://doi.org/10.1109/ICCES51350.2021.9489209
Jain R, Jain M, Jain R, Madan S. Human Computer Interaction-Hand Gesture Recognition. Advanced Journal of Graduate Research, 2022; 11(1): 1-9. https://doi.org/10.21467/ajgr.11.1.1-9
Gourob JH, Raxit S, Hasan A. A Robotic Hand: Controlled With Vision Based Hand Gesture Recognition System. In 2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI) 2021; pp. 1-4. IEEE.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2021 Bala Murali Gunji, Nikhil M. Bhargav, Amrita Dey, Isahak Karajagi Zeeshan Mohammed, Sachdev Sathyajith