Hidden Markov model for human to computer interaction: a study on human hand gesture recognition
Artificial Intelligence Review
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Sign language number recognition system lays down foundation for handshape recognition which addresses real and current problems in signing in the deaf community and leads to practical applications. The input for the sign language number recognition system is 5000 Filipino Sign Language number video file with 640 x 480 pixels frame size and 15 frame/second. The color-coded gloves uses less color compared with other color-coded gloves in the existing research. The system extracts important features from the video using multi-color tracking algorithm which is faster than existing color tracking algorithm because it did not use recursive technique. Next, the system learns and recognizes the Filipino Sign Language number in training and testing phase using Hidden Markov Model. The system uses Hidden Markov Model (HMM) for training and testing phase. The feature extraction could track 92.3% of all objects. The recognizer also could recognize Filipino sign language number with 85.52% average accuracy.