Towards automatic annotation of sign language dictionary corpora

Abstract

This paper deals with novel automatic categorization of signs used in sign language dictionaries. The categorization provides additional information about lexical signs interpreted in the form of video files. We design a new method for automatic parameterization of these video files and categorization of the signs from extracted information. The method incorporates advanced image processing for detection and tracking of hands and head of signing character in the input image sequences. For tracking of hands we developed an algorithm based on object detection and discriminative probability models. For the tracking of head we use active appearance model. This method is a very powerful for detection and tracking of human face. We specify feasible conditions of the model enabling to use the extracted parameters for basic categorization of the non-manual component. We introduce an experiment with the automatic categorization determining symmetry, location and contact of hands, shape of mouth, close eyes and others. The result of experiment is primary the categorization of more than 200 signs and discussion of problems and next extension. © 2011 Springer-Verlag.