Communication for differently abled individuals has always been a challenging task. To address this problem, we present a model of an App, developed on the foundation of machine learning and is constructed using TensorFlow and OpenCV. The App would incorporate various signs and translate it into tex
Novel Gesture Based Augmentative and Alternative (AAC) Communication System for The Deaf and Hard of Hearing Individuals
Communication for differently abled individuals has always been a challenging task. To address this problem, we present a model of an App, developed on the foundation of machine learning and is constructed using TensorFlow and OpenCV. The App would incorporate various signs and translate it into text when detected by camera. The scope of the project lies in the fact that it is an application, making it an efficient user interface (UI) design.
To design an efficient system for hard of hearing individuals that will help them in conveying their message with able individuals.
The method we used to build a sign language detector is by working on the TensorFlow object detection API and Python. In the first phase, we collected our images using Python and OpenCV and then we labelled them using the label image package. In the second phase, we trained the TensorFlow for sign language. In the third phase, we detected sign language in real time. In the final stage, we will build a web application, making it an efficient user interface (UI) design.
This system will be an essential tool to bridge the communication gap between able and hearing-impaired people.
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| Quadro P620 V2 Graphics Card | Equipment | 1 | 55000 | 55000 |
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