Towards a Transcription System of Sign Language Video Resources via Motion Trajectory Factorisation

Sign languages are visual languages used by the Deaf community for communication purposes. Whilst recent years have seen a high growth in the quantity of sign language video collections available online, much of this material is hard to access and process due to the lack of associated text-based tagging information and because 'extracting' content directly from video is currently still a very challenging problem. Also limited is the support for the representation and documentation of sign language video resources in terms of sign writing systems. In this paper, we start with a brief survey of existing sign language technologies and we assess their state of the art from the perspective of a sign language digital information processing system. We then introduce our work, focusing on vision-based sign language recognition. We apply the factorisation method to sign language videos in order to factor out the signer's motion from the structure of the hands. We then model the motion of the hands in terms of a weighted combination of linear trajectory basis and apply a set of classifiers on the basis weights for the purpose of recognising meaningful phonological elements of sign language. We demonstrate how these classification results can be used for transcribing sign videos into a written representation for annotation and documentation purposes. Results from our evaluation process indicate the validity of our proposed framework.