Initiatives for Challenges Faced By Developed Countries and India on Green Growth and Sustainable Development in the World
- Authors: Shalin Kumar1, Parul Verma2, Hoshiyar Singh Kanyal3, Praveen Chandra Jha4, Jyoti Rai5
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View Affiliations Hide Affiliations1 Department of Applied Science, Greater Noida Institute of Technology, Greater Noida, (U.P), India 2 Department of Applied Science and Humanities, Ajay Kumar Garg. Engineering College, Ghaziabad, (U.P.), India 3 Department of Computer Science & Engg., Hi Tech Institute of Engineering and Technology, Ghaziabad, (U.P), India 4 Department of Applied Science and Humanities, ITS Engineering College, Greater Noida (U.P.), India 5 Department of Applied Science and Humanities R.D. Engineering College, Ghaziabad, (U.P.), India
- Source: Demystifying Emerging Trends in Green Technology , pp 219-231
- Publication Date: February 2025
- Language: English
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This review article analyzes various sign language recognition methodologies that rely on sign acquisition techniques and sign identification methods. Artificial neural networks are well suited for analyzing gestures, employing visionbased methods, and identifying signs. The Sign Language Recognition (SLR) system is a method for understanding a set of generated signs and converting them into text or speech while preserving the necessary context. The application of gesture recognition can exemplify effective human-machine interactions. Our objective in this study was to develop a model using a convolutional neural network. Consequently, the precision rate stands at approximately 85%. Subsequent efforts should enhance the Image Processing module to facilitate bidirectional communication, namely enabling the system to seamlessly convert between sign language and conventional language in both directions.
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