dc.contributor.author | Tubaiz, Noor Ali | |
dc.contributor.author | Shanableh, Tamer | |
dc.contributor.author | Assaleh, Khaled | |
dc.date.accessioned | 2017-05-01T06:40:25Z | |
dc.date.available | 2017-05-01T06:40:25Z | |
dc.date.issued | 2015 | |
dc.identifier.citation | Tubaiz, N., Shanableh, T., & Assaleh, K. (2015). Glove-Based continuous Arabic sign language recognition in user-dependent mode. IEEE Transactions on Human-Machine Systems, 45(4), 526-533. doi:10.1109/THMS.2015.2406692 | en_US |
dc.identifier.issn | 2168-2291 | |
dc.identifier.uri | http://hdl.handle.net/11073/8820 | |
dc.description.abstract | In this paper we propose a glove-based Arabic sign language recognition system using a novel technique for sequential data classification. We compile a sensor-based dataset of 40 sentences using an 80-word lexicon. In the dataset, hand movements are captured using two DG5-VHand data gloves. Data labeling is performed using a camera to synchronize hand movements with their corresponding sign language words. Low-complexity preprocessing and feature extraction techniques are applied to capture and emphasize the temporal dependency of the data. Subsequently, a Modified k-Nearest Neighbor (MKNN) approach is used for classification. The proposed MKNN makes use of the context of feature vectors for the purpose of accurate classification. The proposed solution achieved a sentence recognition rate of 98.9%. The results are compared against an existing vision-based approach that uses the same set of sentences. The proposed solution is superior in terms of classification rates whilst eliminating restrictions of vision-based systems. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | IEEE | en_US |
dc.relation.uri | http://doi.org/10.1109/THMS.2015.2406692 | en_US |
dc.subject | Sign language recognition | en_US |
dc.subject | Sensor gloves | en_US |
dc.subject | Feature extraction | en_US |
dc.subject | Pattern recognition | en_US |
dc.title | Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode | en_US |
dc.type | Article | en_US |
dc.type | Postprint | en_US |
dc.type | Peer-Reviewed | en_US |
dc.identifier.doi | 10.1109/THMS.2015.2406692 | |