A Comparative Analysis of Techniques and Algorithms for Recognising Sign Language
This is an incremental survey that reviews existing methods to improve communication tools for hearing-impaired people.
The paper conducts a comparative analysis of deep learning techniques for sign language recognition, addressing the need for affordable and accessible vision-based systems to aid hearing-impaired individuals, but does not report specific experimental results or numbers.
Sign language is a visual language that enhances communication between people and is frequently used as the primary form of communication by people with hearing loss. Even so, not many people with hearing loss use sign language, and they frequently experience social isolation. Therefore, it is necessary to create human-computer interface systems that can offer hearing-impaired people a social platform. Most commercial sign language translation systems now on the market are sensor-based, pricey, and challenging to use. Although vision-based systems are desperately needed, they must first overcome several challenges. Earlier continuous sign language recognition techniques used hidden Markov models, which have a limited ability to include temporal information. To get over these restrictions, several machine learning approaches are being applied to transform hand and sign language motions into spoken or written language. In this study, we compare various deep learning techniques for recognising sign language. Our survey aims to provide a comprehensive overview of the most recent approaches and challenges in this field.