  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.
