Detecting and Identifying Sign Languages through Visual Features
Caio D. D. Monteiro, Christy Maria Mathew, Ricardo Gutiérrez‐Osuna, Frank M. Shipman III · 2016
The popularity of video sharing sites has encouraged the creation and distribution of sign language (SL) content. Unfortunately, locating SL videos on a desired topic is not a straightforward task. Retrieval depends on the existence and correctness of metadata to indicate that the video contains SL. This problem gets worse when considering a particular type of sign language (e.g. American Sign Language - ASL, British Sign Language - BSL, French Sign Language – LSF, etc.), where metadata needs to be even more specific. To address this problem, we have expanded a previous SL classifier to distinguish videos in different SLs. The new classifier achieves an F1 score of 98% when discriminating between BSL and LSF videos with static backgrounds, and a 70% F1 score when distinguishing between ASL and BSL videos found on popular video sharing sites. Such accuracy with visual features alone is possible when comparing languages with one-handed and two-handed manual alphabets.