In this study, a rule based heuristic method is proposed to recognize the primitive hand shapes of Turkish Sign Language (TID) which are sensed by a Leap Motion device. The hand shape data set was also tested with selected machine learning method (Random Forest), and the results of two approaches were compared. The proposed system required less data than the machine learning method, and its success rate was higher.
Burcak Demircioğlu, Güllü Bülbül, Hatice Köse. 2016. Recognition of Sign Language Hand Shape Primitives With Leap Motion. In Proceedings of the LREC2016 7th Workshop on the Representation and Processing of Sign Languages: Corpus Mining, pages 47–52, Portorož, Slovenia. European Language Resources Association (ELRA).
BibTeX Export
@inproceedings{demircioglu:16005:sign-lang:lrec,
author = {Demircio{\u g}lu, Burcak and B{\"u}lb{\"u}l, G{\"u}ll{\"u} and K{\"o}se, Hatice},
title = {Recognition of Sign Language Hand Shape Primitives With {Leap} {Motion}},
pages = {47--52},
editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna},
booktitle = {Proceedings of the {LREC2016} 7th Workshop on the Representation and Processing of Sign Languages: Corpus Mining},
maintitle = {10th International Conference on Language Resources and Evaluation ({LREC} 2016)},
publisher = {{European Language Resources Association (ELRA)}},
address = {Portoro{\v z}, Slovenia},
day = {28},
month = may,
year = {2016},
language = {english},
url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/16005.pdf}
}