Project title
SignNet
Abstract
SignNet is an advanced real-time sign language recognition system that employs the RandomForest Classifier and MediaPipe framework to interpret American Sign Language from video inputs.
Project outline
The system utilises MediaPipe for hand landmark detection and a RandomForest model for the classification of ASL gestures, emphasizing accuracy in gesture recognition.
The project demonstrates the system's capabilities in real-world scenarios, providing a basis for further research in enhancing communication technologies.
Presentation Feedback
My professor appreciated the extensive research I conducted in the field of sign recognition. She acknowledged the significant effort I put into trial and error to identify solutions that worked seamlessly. She was impressed by the way I presented the project, noting that it showed a lot of dedication. Additionally, she liked that I developed a front-end for the sign recognition task and created a seamless design, which added to the overall effectiveness of the project.
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