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Sign language is the primary language for people with a hearing loss. Signlanguage recognition (SLR) is the automatic recognition of sign language, whichrepresents a challenging problem for computers, though some progress has beenmade recently using deep learning. Huge amounts of data are generally requiredto train deep learning models. However, corresponding datasets are missing forthe majority of sign languages. Transfer learning is a technique to utilize arelated task with an abundance of data available to help solve a target tasklacking sufficient data. Transfer learning has been applied highly successfullyin computer vision and natural language processing. However, much less researchhas been conducted in the field of SLR. This paper investigates how effectivelytransfer learning can be applied to isolated SLR using an inflated 3Dconvolutional neural network as the deep learning architecture. Transferlearning is implemented by pre-training a network on the American Sign Languagedataset MS-ASL and subsequently fine-tuning it separately on three differentsizes of the German Sign Language dataset SIGNUM. The results of theexperiments give clear empirical evidence that transfer learning can beeffectively applied to isolated SLR. The accuracy performances of the networksapplying transfer learning increased substantially by up to 21% as compared tothe baseline models that were not pre-trained on the MS-ASL dataset.

Application of Transfer Learning to Sign Language Recognition using an Inflated 3D Deep Convolutional Neural Network