Action Recognition for American Sign Language
This work addresses the challenge of dynamic sign recognition for ASL users, but it is incremental as it builds on existing transfer learning and deep neural network methods.
The research tackled the problem of recognizing American Sign Language from dynamic hand gestures, achieving accuracies of 0.86 and 0.71 using DenseNet201 and LSTM with 12-frame video sequences.
In this research, we present our findings to recognize American Sign Language from series of hand gestures. While most researches in literature focus only on static handshapes, our work target dynamic hand gestures. Since dynamic signs dataset are very few, we collect an initial dataset of 150 videos for 10 signs and an extension of 225 videos for 15 signs. We apply transfer learning models in combination with deep neural networks and background subtraction for videos in different temporal settings. Our primarily results show that we can get an accuracy of $0.86$ and $0.71$ using DenseNet201, LSTM with video sequence of 12 frames accordingly.