Enhancing Communication: Real-Time Sign Language Detection with Deep Learning and Python
Keywords:
Hard-Of-Hearing, Machine Learning Techniques, Convolutional Neural Network, Recurrent Neural Network, Communication, Diverse Sign Language
Abstract
Deaf and hard-of-hearing people use sign language detection to communicate. Sign language is crucial for deaf and hard of hearing people to communicate. Recent advances in computer vision and machine learning have enabled sign language gesture recognition and decipherment. Abstract: Deep learning and computer vision techniques for sign language identification systems are studied and developed. Insufficient datasets and regional sign language gesture variations are discussed in this research. The suggested methods improve sign language recognition system precision and responsiveness, improving deaf community accessibility and inclusivity. Deploying the model on powerful hardware and using TensorFlow's GPU support allows low-latency sign identification for real-world applications. Our experiments show that the system can recognize sign motions in real time with high accuracy and minimal latency. Deaf and hard-of-hearing people can communicate and live better with this technology, making sign language more inclusive. Deep Learning, TensorFlow, CNN, Real-Time, Gesture Recognition, Video Processing, Machine Learning, Low Latency, Human-Computer Interaction, Diverse Sign Language Dataset, RNN are used in Sign Language Detection (SLD).
References
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C. L. Albarracín, S. Venkatesan, A. Y. Torres, P. Yánez-Moretta, and J. C. J. Vargas, “Exploration on cloud computing techniques and its energy concern,” MSEA, vol. 72, no. 1, pp. 749–758, Feb. 2023.
G. Haro-Sosa and S. Venkatesan, “Personified health care transitions with automated doctor appointment system: Logistics,” Journal of Pharmaceutical Negative Results, pp. 2832–2839, 2023.
S. Venkatesan and Z. Rehman, “The power of 5G networks and emerging technology and innovation: Overcoming ongoing century challenges,” Ion Exchange and Adsorption, vol. 23, no. 1, 2023.
J. Cruz Ángeles, The legal-community obligations of the large digital service provider platforms in the metaverse era, Cuad. transnational law , vol. 14, no. 2, p. 294-318, 2022.
J. Cruz Ángeles, The guardians of access to the metaverse. (Re)thinking the Competition Law of the European Union, Cuad. transnational law , vol. 15, no. 1, p. 275-296, 2023.
S. Venkatesan, “Challenges of datafication: Theoretical, training, and communication aspects of artificial intelligence,” Ion Exchange and Adsorption, vol. 23, no. 1, 2023.
S. Venkatesan, “Design an intrusion detection system based on feature selection using ML algorithms,” MSEA, vol. 72, no. 1, pp. 702–710, Feb. 2023.
S. Venkatesan, “Identification protocol heterogeneous systems in cloud computing,” MSEA, vol. 72, no. 1, pp. 615–621, Feb. 2023.
E. Vashishtha and H. Kapoor, "Enhancing patient experience by automating and transforming free text into actionable consumer insights: a natural language processing (NLP) approach," International Journal of Health Sciences and Research, vol. 13, no. 10, pp. 275-288, Oct. 2023.
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Published
2024-10-11
How to Cite
Rajest, S. S., & Regin, R. (2024). Enhancing Communication: Real-Time Sign Language Detection with Deep Learning and Python. Central Asian Journal of Medical and Natural Science, 5(4), 940-951. Retrieved from https://cajmns.centralasianstudies.org/index.php/CAJMNS/article/view/2638
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Articles
