@inproceedings{cooper:10039:sign-lang:lrec,
  author    = {Cooper, Helen and Bowden, Richard},
  title     = {Sign Language Recognition using Linguistically Derived Sub-units},
  pages     = {57--60},
  editor    = {Dreuw, Philippe and Efthimiou, Eleni and Hanke, Thomas and Johnston, Trevor and Mart{\'i}nez Ruiz, Gregorio and Schembri, Adam},
  booktitle = {Proceedings of the {LREC2010} 4th Workshop on the Representation and Processing of Sign Languages: Corpora and Sign Language Technologies},
  maintitle = {7th International Conference on Language Resources and Evaluation ({LREC} 2010)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Valletta, Malta},
  day       = {22--23},
  month     = may,
  year      = {2010},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/10039.html},
  abstract  = {This work proposes to learn linguistically-derived sub-unit classifiers for sign language.  The responses of these classifiers can be combined by Markov models, producing efficient sign-level recognition.  Tracking is used to create vectors of hand positions per frame as inputs for sub-unit classifiers learnt using AdaBoost.  Grid-like classifiers are built around specific elements of the tracking vector to model the placement of the hands.  Comparative classifiers encode the positional relationship between the hands.  Finally, binary-pattern classifiers are applied over the tracking vectors of multiple frames to describe the motion of the hands.  Results for the sub-unit classifiers in isolation are presented, reaching averages over 90{\%}.  Using a simple Markov model to combine the sub-unit classifiers allows sign level classification giving an average of 63{\%}, over a 164 sign lexicon, with no grammatical constraints.}
}