  Sign language is the primary language for people with a hearing loss. Sign
language recognition (SLR) is the automatic recognition of sign language, which
represents a challenging problem for computers, though some progress has been
made recently using deep learning. Huge amounts of data are generally required
to train deep learning models. However, corresponding datasets are missing for
the majority of sign languages. Transfer learning is a technique to utilize a
related task with an abundance of data available to help solve a target task
lacking sufficient data. Transfer learning has been applied highly successfully
in computer vision and natural language processing. However, much less research
has been conducted in the field of SLR. This paper investigates how effectively
transfer learning can be applied to isolated SLR using an inflated 3D
convolutional neural network as the deep learning architecture. Transfer
learning is implemented by pre-training a network on the American Sign Language
dataset MS-ASL and subsequently fine-tuning it separately on three different
sizes of the German Sign Language dataset SIGNUM. The results of the
experiments give clear empirical evidence that transfer learning can be
effectively applied to isolated SLR. The accuracy performances of the networks
applying transfer learning increased substantially by up to 21% as compared to
the baseline models that were not pre-trained on the MS-ASL dataset.
