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Article type: Research Article
Authors: Srivastava, Sangeetaa | Varshney, Ashwanib | Katyal, Supriyab | Kaur, Ravneetc | Gaur, Vibhad; *
Affiliations: [a] Department of Computer Science, Bhaskaracharya College of Applied Sciences, University of Delhi, Delhi, India | [b] Department of Computer Science, University of Delhi, Delhi, India | [c] Department of Electronics, Acharya Narendra Dev College, University of Delhi, Delhi, India | [d] Department of Computer Science, Acharya Narendra Dev College, University of Delhi, Delhi, India
Correspondence: [*] Corresponding author. Vibha Gaur, Department of Computer Science, University of Delhi Acharya Narendra Dev College, India. E-mail: [email protected].
Abstract: The government has established special schools to cater to the needs of children with disabilities but they are often segregated rather than receiving equitable opportunities. Artificial Intelligence has opened new ways to promote special education with advanced learning tools. These tools enable to adapt to a typical classroom set up for all the students with or without disabilities. To ensure social equity and the same classroom experience, a coherent solution is envisioned for inclusive education. This paper aims to propose a cost-effective and integrated Smart Learning Assistance (SLA) tool for Inclusive Education using Deep Learning and Computer Vision techniques. It comprises speech to text and sign language conversion for hearing impaired students, sign language to text conversion for speech impaired students, and Braille to text for communicating with visually impaired students. The tool assists differently-abled students to make use of various teaching-learning opportunities conferred to them and ensures convenient two-way communication with the instructor and peers in the classroom thus makes learning easier.
Keywords: Inclusive classroom, image processing, computer vision, deep learning, artificial intelligence
DOI: 10.3233/JIFS-210075
Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11981-11994, 2021
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