Learning Signs from Subtitles: A Weakly Supervised Approach to Sign Language Recognition

This paper introduces a fully-automated, unsupervised method to recognise sign from subtitles. It does this by using data mining to align correspondences in sections of videos. Based on head and hand tracking, a novel temporally constrained adaptation of apriori mining is used to extract similar regions of video, with the aid of a proposed contextual negative selection method. These regions are refined in the temporal domain to isolate the occurrences of similar signs in each example. The system is shown to automatically identify and segment signs from standard news broadcasts containing a variety of topics.

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Helen Cooper, Richard Bowden
• Keywords: Computer Vision, CVPR 2009, Mining, Sign Language, Weakly Supervised Learning