IndieSign: A Learning Module for Indian Sign Language using Supervised Machine Learning Techniques
Aaditya Kulkarni, Ashwin Raina, Rohit Ramteke, Sayush Kamat, Jyoti Deshmukh · 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · 2022
Globalization is moving at a rapid pace with virtually no borders amongst various cultures and societies, the hearing-impaired face a monumental challenge to be included in the multi-faceted society. There has been a recent push towards introducing inclusiveness policies so that the hearing impaired shall have an easier way to be included in the society. However, as hearing-impaired people are stigmatized as being “diseased”, people do not interact with them. This affects their mental well-being and discourages future interactions. With a recent push towards digitization, it is believed that providing an online learning module can promote inclusiveness between the two parties. The issue of not being taught the concept of a word makes the learning incomplete. This is not only restricted to day-to-day concepts but also niche categories like technology, agriculture etc. To bridge this gap, this paper presents a unique model through which any person would be able to learn the words along with its concepts and their signage. As people with no hearing impairment tend to not learn sign language, this paper discusses methods through which learning sign language can be made unexacting.