Mobile Application-Based Sign Language Detector for Deaf People

Mobile Application-Based Sign Language Detector for Deaf People

Copyright: © 2023 |Pages: 22
DOI: 10.4018/978-1-6684-8582-8.ch018
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Abstract

In everyday life, we use hand gestures for several activities, like calling someone or showing directions to somebody, and don't even realise or consciously acknowledge them as a part of life. But for people with hearing disabilities, efficiently contributing to society is a much more important part of their everyday routine. They are their major medium of communication and an incredible way for human communication. Vision-based hand gesture recognition procedures have many demonstrated advantages compared with conventional gadgets. In this chapter, the authors discuss various available products to predict what a user is trying to convey through hand gestures using American Sign Language (ASL). While being able to help deaf people, this chapter also focuses on the proposed method or tool that can also be used for the practice of ASL by regular people to learn and practice sign language. This concept can also be implemented in schools easily to design ASL into their curriculum.
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Background Survey

Sharma and Kumar (2021), have used the 3D convolution neural network to recognize the ASL in volumetric data like videos. They have used the Boston ASL LVD (Lexicon video dataset) for their model. The system was used to recognize the ASL in real-time from dynamic videos by converting each video into frames. Pre-processing takes a lot of time, which is the main drawback of this method. The video is first converted into several frames, then the frames are changed to specific sizes for processing, after that the frames are changed into grayscale and if any noise is present that is also removed at this stage and finally the frames are then combined again into a video and could be used for training the model.

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