  Isolated sign recognition from video streams is a challenging problem due to
the multi-modal nature of the signs, where both local and global hand features
and face gestures needs to be attended simultaneously. This problem has
recently been studied widely using deep Convolutional Neural Network (CNN)
based features and Long Short-Term Memory (LSTM) based deep sequence models.
However, the current literature is lack of providing empirical analysis using
Hidden Markov Models (HMMs) with deep features. In this study, we provide a
framework that is composed of three modules to solve isolated sign recognition
problem using different sequence models. The dimensions of deep features are
usually too large to work with HMM models. To solve this problem, we propose
two alternative CNN based architectures as the second module in our framework,
to reduce deep feature dimensions effectively. After extensive experiments, we
show that using pretrained Resnet50 features and one of our CNN based dimension
reduction models, HMMs can classify isolated signs with 90.15% accuracy in
Montalbano dataset using RGB and Skeletal data. This performance is comparable
with the current LSTM based models. HMMs have fewer parameters and can be
trained and run on commodity computers fast, without requiring GPUs. Therefore,
our analysis with deep features show that HMMs could also be utilized as well
as deep sequence models in challenging isolated sign recognition problem.
