Volume 14 • Issue 2 • PP: 127-139 • 2025
Artificial Intelligence based Automated Sign Gesture Recognition Solutions for Visually Challenged People
Open Access & Copyright
© 2025 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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Gesture recognition is employed in human-machine communications, enhancing human life with impairments or who depend on non-verbal instructions. Hand gestures role an important role in the field of assistive technology for persons with visual impairments, whereas an optimum user communication design is of major importance. Many authors with substantial development for gesture recognition modeled several methods by using deep learning (DL) methods. This article introduces a Robust Gesture Sign Language Recognition Using Chicken Earthworm Optimization with Deep Learning (RSLR-CEWODL) approach. The projected RSLR-CEWODL algorithm majorly focuses on the recognition and classification of sign language. To accomplish this, the presented RSLR-CEWODL technique utilizes a residual network (ResNet-101) model for feature extraction. For optimal hyper parameter tuning process, the presented RSLR-CEWODL algorithm exploits the CEWO algorithm. Besides, the RSLR-CEWODL technique uses a whale optimization algorithm (WOA) with deep belief network (DBN) method for the sign language recognition method. The simulation result of the RSLR-CEWODL algorithm is tested using sign language datasets and the outcome was measured under various measures. The simulation values demonstrated the enhancements of the RSLR-CEWODL technique over other methodologies.
Keywords
References
[1] Gangrade, J. and Bharti, J., 2023. Vision-based hand gesture recognition for Indian sign language using convolution neural network. IETE Journal of Research, 69(2), pp.723-732.
[2] Ryumin, D., Ivanko, D. and Ryumina, E., 2023. Audio-Visual Speech and Gesture Recognition by Sensors of Mobile Devices. Sensors, 23(4), p.2284.
[3] Alrowais, F., Marzouk, R., Al-Wesabi, F.N. and Hilal, A.M., 2023. Hand Gesture Recognition for Disabled People Using Bayesian Optimization with Transfer Learning. Intelligent Automation & Soft Computing, 36(3).
[4] Lee, J.W. and Yu, K.H., 2023. Wearable Drone Controller: Machine Learning-Based Hand Gesture Recognition and Vibrotactile Feedback. Sensors, 23(5), p.2666.
[5] Lu, W.X., Fang, P., Zhu, M.L., Zhu, Y.R., Fan, X.J., Zhu, T.C., Zhou, X., Wang, F.X., Chen, T. and Sun, L.N., 2023. Artificial Intelligence–Enabled Gesture‐Language‐Recognition Feedback System Using Strain‐Sensor‐Arrays‐Based Smart Glove. Advanced Intelligent Systems, p.2200453.
[6] Adaloglou, N., Chatzis, T., Papastratis, I., Stergioulas, A., Papadopoulos, G.T., Zacharopoulou, V., Xydopoulos, G.J., Atzakas, K., Papazachariou, D. and Daras, P., 2021. A comprehensive study on deep learning-based methods for sign language recognition. IEEE Transactions on Multimedia, 24, pp.1750-1762.
[7] John, J. and Sherif, B.V., 2022. Hand Landmark-Based Sign Language Recognition Using Deep Learning. In Machine Learning and Autonomous Systems (pp. 147-157). Springer, Singapore.
[8] Rastgoo, R., Kiani, K. and Escalera, S., 2021. Sign language recognition: A deep survey. Expert Systems with Applications, 164, p.113794.
[9] Gupta, R., Golaya, S. and Srinivasan, R., 2022, March. Transfer-Learning Based User-Personalization of Indian Sign Language Recognition System. In 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS) (Vol. 1, pp. 615-620). IEEE.
[10] Gao, Y., Jia, C., Chen, H. and Jiang, X., 2020. Chinese fingerspelling sign language recognition using a nine-layer convolutional neural network. EAI Endorsed Transactions on e-Learning, 7(20).
[11] Mannan, A., Abbasi, A., Javed, A.R., Ahsan, A., Gadekallu, T.R. and Xin, Q., 2022. Hypertuned deep convolutional neural network for sign language recognition. Computational Intelligence and Neuroscience, 2022.
[12] Sharma, S., Gupta, R. and Kumar, A., 2021. Continuous sign language recognition using isolated signs data and deep transfer learning. Journal of Ambient Intelligence and Humanized Computing, pp.1-12.
[13] Kothadiya, D., Bhatt, C., Sapariya, K., Patel, K., Gil-González, A.B. and Corchado, J.M., 2022. Deepsign: Sign Language Detection and Recognition Using Deep Learning. Electronics, 11(11), p.1780.
[14] Du, Y., Xie, P., Wang, M., Hu, X., Zhao, Z. and Liu, J., 2022. Full Transformer Network with Masking Future for Word-Level Sign Language Recognition. Neurocomputing.
[15] Lu, J., Nguyen, M. and Yan, W.Q., 2021, January. Sign language recognition from digital videos using deep learning methods. In International Symposium on Geometry and Vision (pp. 108-118). Springer, Cham.
[16] Arora, S., Gupta, A., Jain, R. and Nayyar, A., 2021. Optimization of the CNN model for hand sign language recognition using Adam optimization technique. In Micro-electronics and telecommunication engineering (pp. 89-104). Springer, Singapore.
[17] Papastratis, I., Dimitropoulos, K. and Daras, P., 2021. Continuous sign language recognition through a context-aware generative adversarial network. Sensors, 21(7), p.2437.
[18] Ji, L., Mao, R., Wu, J., Ge, C., Xiao, F., Xu, X., Xie, L. and Gu, X., 2022. Deep Convolutional Neural Network for Nasopharyngeal Carcinoma Discrimination on MRI by Comparison of Hierarchical and Simple Layered Convolutional Neural Networks. Diagnostics, 12(10), p.2478.
[19] Vijay, M. and Indumathi, G., 2021. Deep belief network-based hybrid model for multimodal biometric system for futuristic security applications. Journal of Information Security and Applications, 58, p.102707.
[20] Cheng, J., Xu, J., Chen, W. and Song, B., 2022. Locating and sizing method of electric vehicle charging station based on Improved Whale Optimization Algorithm. Energy Reports, 8, pp.4386-4400.
[21] Wadhawan, A. and Kumar, P., 2020. Deep learning-based sign language recognition system for static signs. Neural computing and applications, 32(12), pp.7957-7968.
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