Recognition of Two-Handed Arabic Signs Using the CyberGlove

Abstract Sign language maps letters, words, and expressions of a certain language to a set of hand gestures enabling an individual to communicate by using hands and gestures rather than by speaking. Systems capable of recognizing sign-language symbols can be used for communication with the hearing-i...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Mohandes, Mohamed A. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2012

Schlagwörter:	Arabic sign language Recognition Support vector machine Principal component analysis CyberGlove Flock of Birds

Übergeordnetes Werk:	Enthalten in: The Arabian journal for science and engineering - Berlin : Springer, 2011, 38(2012), 3 vom: 23. Nov., Seite 669-677
Übergeordnetes Werk:	volume:38 ; year:2012 ; number:3 ; day:23 ; month:11 ; pages:669-677

Links:	Volltext

DOI / URN:	10.1007/s13369-012-0378-z

Katalog-ID:	SPR031731805

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520			\|a Abstract Sign language maps letters, words, and expressions of a certain language to a set of hand gestures enabling an individual to communicate by using hands and gestures rather than by speaking. Systems capable of recognizing sign-language symbols can be used for communication with the hearing-impaired. This paper represents the first attempt to recognize two-handed signs from the Unified Arabic Sign Language Dictionary using the CyberGlove and support vector machines (SVMs). 20 samples from each of 100 two-handed signs were collected from two adult signers. Because the signs are of different lengths, time division is used to standardize sign length. The duration of every sign is divided into a specific number of segments, and the mean and standard deviation of each segment are used to represent the signal in the segment. After pre-processing, principal component analysis is used for feature extraction. For recognition, a SVM is trained on 15 samples from each sign. The performance is obtained by testing the trained SVM on the remaining five samples from each sign. A recognition rate of 99.6% on the testing data is obtained.
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allfields	10.1007/s13369-012-0378-z doi (DE-627)SPR031731805 (SPR)s13369-012-0378-z-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Mohandes, Mohamed A. verfasserin aut Recognition of Two-Handed Arabic Signs Using the CyberGlove 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Sign language maps letters, words, and expressions of a certain language to a set of hand gestures enabling an individual to communicate by using hands and gestures rather than by speaking. Systems capable of recognizing sign-language symbols can be used for communication with the hearing-impaired. This paper represents the first attempt to recognize two-handed signs from the Unified Arabic Sign Language Dictionary using the CyberGlove and support vector machines (SVMs). 20 samples from each of 100 two-handed signs were collected from two adult signers. Because the signs are of different lengths, time division is used to standardize sign length. The duration of every sign is divided into a specific number of segments, and the mean and standard deviation of each segment are used to represent the signal in the segment. After pre-processing, principal component analysis is used for feature extraction. For recognition, a SVM is trained on 15 samples from each sign. The performance is obtained by testing the trained SVM on the remaining five samples from each sign. A recognition rate of 99.6% on the testing data is obtained. Arabic sign language (dpeaa)DE-He213 Recognition (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 CyberGlove (dpeaa)DE-He213 Flock of Birds (dpeaa)DE-He213 Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 38(2012), 3 vom: 23. Nov., Seite 669-677 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:38 year:2012 number:3 day:23 month:11 pages:669-677 https://dx.doi.org/10.1007/s13369-012-0378-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 38 2012 3 23 11 669-677
spelling	10.1007/s13369-012-0378-z doi (DE-627)SPR031731805 (SPR)s13369-012-0378-z-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Mohandes, Mohamed A. verfasserin aut Recognition of Two-Handed Arabic Signs Using the CyberGlove 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Sign language maps letters, words, and expressions of a certain language to a set of hand gestures enabling an individual to communicate by using hands and gestures rather than by speaking. Systems capable of recognizing sign-language symbols can be used for communication with the hearing-impaired. This paper represents the first attempt to recognize two-handed signs from the Unified Arabic Sign Language Dictionary using the CyberGlove and support vector machines (SVMs). 20 samples from each of 100 two-handed signs were collected from two adult signers. Because the signs are of different lengths, time division is used to standardize sign length. The duration of every sign is divided into a specific number of segments, and the mean and standard deviation of each segment are used to represent the signal in the segment. After pre-processing, principal component analysis is used for feature extraction. For recognition, a SVM is trained on 15 samples from each sign. The performance is obtained by testing the trained SVM on the remaining five samples from each sign. A recognition rate of 99.6% on the testing data is obtained. Arabic sign language (dpeaa)DE-He213 Recognition (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 CyberGlove (dpeaa)DE-He213 Flock of Birds (dpeaa)DE-He213 Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 38(2012), 3 vom: 23. Nov., Seite 669-677 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:38 year:2012 number:3 day:23 month:11 pages:669-677 https://dx.doi.org/10.1007/s13369-012-0378-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 38 2012 3 23 11 669-677
allfields_unstemmed	10.1007/s13369-012-0378-z doi (DE-627)SPR031731805 (SPR)s13369-012-0378-z-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Mohandes, Mohamed A. verfasserin aut Recognition of Two-Handed Arabic Signs Using the CyberGlove 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Sign language maps letters, words, and expressions of a certain language to a set of hand gestures enabling an individual to communicate by using hands and gestures rather than by speaking. Systems capable of recognizing sign-language symbols can be used for communication with the hearing-impaired. This paper represents the first attempt to recognize two-handed signs from the Unified Arabic Sign Language Dictionary using the CyberGlove and support vector machines (SVMs). 20 samples from each of 100 two-handed signs were collected from two adult signers. Because the signs are of different lengths, time division is used to standardize sign length. The duration of every sign is divided into a specific number of segments, and the mean and standard deviation of each segment are used to represent the signal in the segment. After pre-processing, principal component analysis is used for feature extraction. For recognition, a SVM is trained on 15 samples from each sign. The performance is obtained by testing the trained SVM on the remaining five samples from each sign. A recognition rate of 99.6% on the testing data is obtained. Arabic sign language (dpeaa)DE-He213 Recognition (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 CyberGlove (dpeaa)DE-He213 Flock of Birds (dpeaa)DE-He213 Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 38(2012), 3 vom: 23. Nov., Seite 669-677 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:38 year:2012 number:3 day:23 month:11 pages:669-677 https://dx.doi.org/10.1007/s13369-012-0378-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 38 2012 3 23 11 669-677
allfieldsGer	10.1007/s13369-012-0378-z doi (DE-627)SPR031731805 (SPR)s13369-012-0378-z-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Mohandes, Mohamed A. verfasserin aut Recognition of Two-Handed Arabic Signs Using the CyberGlove 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Sign language maps letters, words, and expressions of a certain language to a set of hand gestures enabling an individual to communicate by using hands and gestures rather than by speaking. Systems capable of recognizing sign-language symbols can be used for communication with the hearing-impaired. This paper represents the first attempt to recognize two-handed signs from the Unified Arabic Sign Language Dictionary using the CyberGlove and support vector machines (SVMs). 20 samples from each of 100 two-handed signs were collected from two adult signers. Because the signs are of different lengths, time division is used to standardize sign length. The duration of every sign is divided into a specific number of segments, and the mean and standard deviation of each segment are used to represent the signal in the segment. After pre-processing, principal component analysis is used for feature extraction. For recognition, a SVM is trained on 15 samples from each sign. The performance is obtained by testing the trained SVM on the remaining five samples from each sign. A recognition rate of 99.6% on the testing data is obtained. Arabic sign language (dpeaa)DE-He213 Recognition (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 CyberGlove (dpeaa)DE-He213 Flock of Birds (dpeaa)DE-He213 Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 38(2012), 3 vom: 23. Nov., Seite 669-677 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:38 year:2012 number:3 day:23 month:11 pages:669-677 https://dx.doi.org/10.1007/s13369-012-0378-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 38 2012 3 23 11 669-677
allfieldsSound	10.1007/s13369-012-0378-z doi (DE-627)SPR031731805 (SPR)s13369-012-0378-z-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Mohandes, Mohamed A. verfasserin aut Recognition of Two-Handed Arabic Signs Using the CyberGlove 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Sign language maps letters, words, and expressions of a certain language to a set of hand gestures enabling an individual to communicate by using hands and gestures rather than by speaking. Systems capable of recognizing sign-language symbols can be used for communication with the hearing-impaired. This paper represents the first attempt to recognize two-handed signs from the Unified Arabic Sign Language Dictionary using the CyberGlove and support vector machines (SVMs). 20 samples from each of 100 two-handed signs were collected from two adult signers. Because the signs are of different lengths, time division is used to standardize sign length. The duration of every sign is divided into a specific number of segments, and the mean and standard deviation of each segment are used to represent the signal in the segment. After pre-processing, principal component analysis is used for feature extraction. For recognition, a SVM is trained on 15 samples from each sign. The performance is obtained by testing the trained SVM on the remaining five samples from each sign. A recognition rate of 99.6% on the testing data is obtained. Arabic sign language (dpeaa)DE-He213 Recognition (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 CyberGlove (dpeaa)DE-He213 Flock of Birds (dpeaa)DE-He213 Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 38(2012), 3 vom: 23. Nov., Seite 669-677 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:38 year:2012 number:3 day:23 month:11 pages:669-677 https://dx.doi.org/10.1007/s13369-012-0378-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 38 2012 3 23 11 669-677
language	English
source	Enthalten in The Arabian journal for science and engineering 38(2012), 3 vom: 23. Nov., Seite 669-677 volume:38 year:2012 number:3 day:23 month:11 pages:669-677
sourceStr	Enthalten in The Arabian journal for science and engineering 38(2012), 3 vom: 23. Nov., Seite 669-677 volume:38 year:2012 number:3 day:23 month:11 pages:669-677
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Arabic sign language Recognition Support vector machine Principal component analysis CyberGlove Flock of Birds
dewey-raw	600
isfreeaccess_bool	false
container_title	The Arabian journal for science and engineering
authorswithroles_txt_mv	Mohandes, Mohamed A. @@aut@@
publishDateDaySort_date	2012-11-23T00:00:00Z
hierarchy_top_id	588780731
dewey-sort	3600
id	SPR031731805
language_de	englisch
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author	Mohandes, Mohamed A.
spellingShingle	Mohandes, Mohamed A. ddc 600 bkl 31.00 misc Arabic sign language misc Recognition misc Support vector machine misc Principal component analysis misc CyberGlove misc Flock of Birds Recognition of Two-Handed Arabic Signs Using the CyberGlove
authorStr	Mohandes, Mohamed A.
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topic_title	600 500 ASE 31.00 bkl Recognition of Two-Handed Arabic Signs Using the CyberGlove Arabic sign language (dpeaa)DE-He213 Recognition (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 CyberGlove (dpeaa)DE-He213 Flock of Birds (dpeaa)DE-He213
topic	ddc 600 bkl 31.00 misc Arabic sign language misc Recognition misc Support vector machine misc Principal component analysis misc CyberGlove misc Flock of Birds
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title	Recognition of Two-Handed Arabic Signs Using the CyberGlove
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title_full	Recognition of Two-Handed Arabic Signs Using the CyberGlove
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title_sort	recognition of two-handed arabic signs using the cyberglove
title_auth	Recognition of Two-Handed Arabic Signs Using the CyberGlove
abstract	Abstract Sign language maps letters, words, and expressions of a certain language to a set of hand gestures enabling an individual to communicate by using hands and gestures rather than by speaking. Systems capable of recognizing sign-language symbols can be used for communication with the hearing-impaired. This paper represents the first attempt to recognize two-handed signs from the Unified Arabic Sign Language Dictionary using the CyberGlove and support vector machines (SVMs). 20 samples from each of 100 two-handed signs were collected from two adult signers. Because the signs are of different lengths, time division is used to standardize sign length. The duration of every sign is divided into a specific number of segments, and the mean and standard deviation of each segment are used to represent the signal in the segment. After pre-processing, principal component analysis is used for feature extraction. For recognition, a SVM is trained on 15 samples from each sign. The performance is obtained by testing the trained SVM on the remaining five samples from each sign. A recognition rate of 99.6% on the testing data is obtained.
abstractGer	Abstract Sign language maps letters, words, and expressions of a certain language to a set of hand gestures enabling an individual to communicate by using hands and gestures rather than by speaking. Systems capable of recognizing sign-language symbols can be used for communication with the hearing-impaired. This paper represents the first attempt to recognize two-handed signs from the Unified Arabic Sign Language Dictionary using the CyberGlove and support vector machines (SVMs). 20 samples from each of 100 two-handed signs were collected from two adult signers. Because the signs are of different lengths, time division is used to standardize sign length. The duration of every sign is divided into a specific number of segments, and the mean and standard deviation of each segment are used to represent the signal in the segment. After pre-processing, principal component analysis is used for feature extraction. For recognition, a SVM is trained on 15 samples from each sign. The performance is obtained by testing the trained SVM on the remaining five samples from each sign. A recognition rate of 99.6% on the testing data is obtained.
abstract_unstemmed	Abstract Sign language maps letters, words, and expressions of a certain language to a set of hand gestures enabling an individual to communicate by using hands and gestures rather than by speaking. Systems capable of recognizing sign-language symbols can be used for communication with the hearing-impaired. This paper represents the first attempt to recognize two-handed signs from the Unified Arabic Sign Language Dictionary using the CyberGlove and support vector machines (SVMs). 20 samples from each of 100 two-handed signs were collected from two adult signers. Because the signs are of different lengths, time division is used to standardize sign length. The duration of every sign is divided into a specific number of segments, and the mean and standard deviation of each segment are used to represent the signal in the segment. After pre-processing, principal component analysis is used for feature extraction. For recognition, a SVM is trained on 15 samples from each sign. The performance is obtained by testing the trained SVM on the remaining five samples from each sign. A recognition rate of 99.6% on the testing data is obtained.
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container_issue	3
title_short	Recognition of Two-Handed Arabic Signs Using the CyberGlove
url	https://dx.doi.org/10.1007/s13369-012-0378-z
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doi_str	10.1007/s13369-012-0378-z
up_date	2024-07-04T01:01:42.935Z
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score	7.399728

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Recognition of Two-Handed Arabic Signs Using the CyberGlove

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