AlphabetSign:Gamifying Signs with DeepLearning
Pilli.Akshitha, Chakilam.Akshitha, Gade.Athwika, Pasupuleti.Bhanu Srinija, Manne.Bharghavi, Sankaran Ramesh Kumar
It has always been difficult to close the communication gap between hearing and deaf or hard-of-hearing people. There are now chances to overcome this obstacle because of developments in computer vision and natural language processing. We provide an interactive game application to help people learn sign language through text-to-image conversion. By accurately converting text into sign graphics, the core capability makes learning interesting and instructive. Our interactive, text-input-friendly "AlphabetSigns: Gamifying Sign Language with Deep Learning" was created in Python using Tkinter. The goal of this text-to-sign conversion game is to provide people with an enjoyable and engaging approach to learn sign language, all while acting as an accessible educational resource. The application ensures a simple and inclusive learning experience for users of all ages by removing the complications involved with voice recognition and camera-based gesture interpretation, and instead concentrating on translating text inputs into visuals in sign language. The creative method used in this application advances the development of assistive educational technologies in addition to encouraging the broad use of sign language. With this study, we show how machine learning and interactive gaming components can be used to enhance language acquisition and communication among the deaf and hard-of-hearing population.
Article Details
Unique Paper ID: 165008

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 2469 - 2472
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