<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The world has been lately witnessing a landmark revolution in technology whereby more research and development is going into producing devices that are able to respond to human emotions, motions and behavior. These days, mobile phones are able to capture photos when the user is smiling or gesturing towards them. By making certain gestures, a user is able to control smart televisions and computers at the comfort of their homes, without the need of extra interface devices. The interaction between man and machines is being improved and made as natural as possible. More so, the application of hand gesture technology is finding use in making communication between people with hearing difficulties and those without, possible. This research presents the techniques that have made this possible. Most of these techniques however are in development stages. This is because of the significant challenges faced in providing this solution. First, there are over 30,000 sign language gestures which is quite a headache when trying to integrate them into a recognition system. Secondly, despite the fact that there are standard ways of making the sign language gestures, people have variations to them. Developers hence have to make allowance for these variations and in effect compromise on the accuracy of the system. This research evaluates the various algorithms developed, and tries to propose an algorithm that strikes the best balance between accuracy and allowance for variations in the sign language gestures.</p></abstract>

The world has been lately witnessing a landmark revolution in technology whereby more research and development is going into producing devices that are able to respond to human emotions, motions and behavior. These days, mobile phones are able to capture photos when the user is smiling or gesturing towards them. By making certain gestures, a user is able to control smart televisions and computers at the comfort of their homes, without the need of extra interface devices. The interaction between man and machines is being improved and made as natural as possible. More so, the application of hand gesture technology is finding use in making communication between people with hearing difficulties and those without, possible. This research presents the techniques that have made this possible. Most of these techniques however are in development stages. This is because of the significant challenges faced in providing this solution. First, there are over 30,000 sign language gestures which is quite a headache when trying to integrate them into a recognition system. Secondly, despite the fact that there are standard ways of making the sign language gestures, people have variations to them. Developers hence have to make allowance for these variations and in effect compromise on the accuracy of the system. This research evaluates the various algorithms developed, and tries to propose an algorithm that strikes the best balance between accuracy and allowance for variations in the sign language gestures.

On Sign Language Toolbox Aid

Land Forces Academy Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"On Sign Language Toolbox Aid"}

The world has been lately witnessing a landmark revolution in technology whereby more research and development is going into producing devices that are able to...

On Sign Language Toolbox Aid

Pubblicato online: 12 mar 2020

Pagine: 47 - 60

DOI: https://doi.org/10.2478/raft-2020-0007

Parole chiave
sign language, gestures, toolbox aid

© 2020 Mohanad Abdulhamid et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

On Sign Language Toolbox Aid

Pubblicato online: 12 mar 2020

Pagine: 47 - 60

DOI: https://doi.org/10.2478/raft-2020-0007

Parole chiavesign language, gestures, toolbox aid

© 2020 Mohanad Abdulhamid et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parole chiave
sign language, gestures, toolbox aid