Experimental Framework Design for Sign Language Automatic Recognition

Abstract

Automatic sign language recognition (ASLR) is quite a complex task, not only for the intrinsic difficulty of automatic video information retrieval, but also because almost every sign language (SL) can be considered as an under-resourced language when it comes to language technology. Spanish sign language (SSL) is one of those under-resourced languages. Developing technology for SSL implies a number of technical challenges that must be tackled down in a structured and sequential manner. In this paper, the problem of how to design an experimental framework for machine-learning-based ASLR is addressed. In our review of existing datasets, our main conclusion is that there is a need for high-quality data. We therefore propose some guidelines on how to conduct the acquisition and annotation of an SSL dataset. These guidelines were developed after conducting some preliminary ASLR experiments with small and limited subsets of existing datasets.