Real Time Assistive Interpreter for Deaf Community Over Machine Learning
Pages : 362-368
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Abstract
After studying multiple research papers on machine learning and real time hand gesture recognition many systems worked only on sign recognition for number generation and sign recognition for A-Z alphabet generation modules. By considering communication interface for deaf and dumb community one way communication is not enough. So there is need of two way communication interface to overcome communication gap. Existing module is failed to fill this barrier of communication. We are going to overcome existing communication barrier by providing two way communications for deaf and dumb peoples. The current work is based on American Sign Language dataset of letters A-Z and 0-9. The propose work is going to invent word dataset for common words and making interpreter for communication with the help of Convolutional neural network (CNN). We are going to focus on accuracy factor and time complexity.
Keywords: Convolutional Neural Network, Deaf community, Hand Gesture Recognition, Indian Sign Language