Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode
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 lab...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| التنسيق: | article |
| منشور في: |
2015
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/11073/8820 |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513438467751936 |
|---|---|
| author | Tubaiz, Noor Ali |
| author2 | Shanableh, Tamer Assaleh, Khaled |
| author2_role | author author |
| author_facet | Tubaiz, Noor Ali Shanableh, Tamer Assaleh, Khaled |
| author_role | author |
| dc.creator.none.fl_str_mv | Tubaiz, Noor Ali Shanableh, Tamer Assaleh, Khaled |
| dc.date.none.fl_str_mv | 2015 2017-05-01T06:40:25Z 2017-05-01T06:40:25Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 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 2168-2291 http://hdl.handle.net/11073/8820 10.1109/THMS.2015.2406692 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | http://doi.org/10.1109/THMS.2015.2406692 |
| dc.subject.none.fl_str_mv | Sign language recognition Sensor gloves Feature extraction Pattern recognition |
| dc.title.none.fl_str_mv | Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode |
| dc.type.none.fl_str_mv | Postprint Peer-Reviewed info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | 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. |
| format | article |
| id | aus_ed4ebf83f1e051a3491c3ea77019e640 |
| identifier_str_mv | 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 2168-2291 10.1109/THMS.2015.2406692 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/8820 |
| publishDate | 2015 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent ModeTubaiz, Noor AliShanableh, TamerAssaleh, KhaledSign language recognitionSensor glovesFeature extractionPattern recognitionIn 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.IEEE2017-05-01T06:40:25Z2017-05-01T06:40:25Z2015PostprintPeer-Reviewedinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfTubaiz, 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.2015.24066922168-2291http://hdl.handle.net/11073/882010.1109/THMS.2015.2406692en_UShttp://doi.org/10.1109/THMS.2015.2406692oai:repository.aus.edu:11073/88202024-08-22T12:08:23Z |
| spellingShingle | Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode Tubaiz, Noor Ali Sign language recognition Sensor gloves Feature extraction Pattern recognition |
| status_str | publishedVersion |
| title | Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode |
| title_full | Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode |
| title_fullStr | Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode |
| title_full_unstemmed | Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode |
| title_short | Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode |
| title_sort | Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode |
| topic | Sign language recognition Sensor gloves Feature extraction Pattern recognition |
| url | http://hdl.handle.net/11073/8820 |