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Sign language is a language that involves a movement of hand gestures. It is a medium for the hearing impaired person (deaf or mute) to communicate with others. However, in order to communicate with the hearing impaired person, the communicator has to have knowledge in sign language. This is to ensure that the message delivered by the hearing impaired person is understood. This project proposes a real time Malaysian sign language detection based on the Convolutional Neural Network (CNN) technique utilizing the You Only Look Once version 3 (YOLOv3) algorithm. Sign language images from web sources and recorded sign language videos by frames were collected. The images were labelled either alphabets or movements. Once the preprocessing phase was completed, the system was trained and tested on the Darknet framework. The system achieved 63 percent accuracy with learning saturation (overfitting) at 7000 iterations. Once it is successfully conducted, this model will be integrated with other platform in the future such as mobile application.

A Real Time Malaysian Sign Language Detection Algorithm Based on YOLOv3