Indian sign language alphabet recognition system using CNN with diffGrad optimizer and stochastic pooling

Abstract India has the largest deaf population in the world and sign language is the principal medium for such persons to share information with normal people and among themselves. Yet, normal people do not have any knowledge of such language. As a result, there is a huge communication barrier betwe...
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Autor*in:	Nandi, Utpal [verfasserIn] Ghorai, Anudyuti Singh, Moirangthem Marjit Changdar, Chiranjit Bhakta, Shubhankar Kumar Pal, Rajat

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Sign language Data augmentation Pooling Optimizer Dropout Convolution Batch normalization

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021

Übergeordnetes Werk:	Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 82(2022), 7 vom: 08. Jan., Seite 9627-9648
Übergeordnetes Werk:	volume:82 ; year:2022 ; number:7 ; day:08 ; month:01 ; pages:9627-9648

Links:	Volltext

DOI / URN:	10.1007/s11042-021-11595-4

Katalog-ID:	SPR049525476

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520			\|a Abstract India has the largest deaf population in the world and sign language is the principal medium for such persons to share information with normal people and among themselves. Yet, normal people do not have any knowledge of such language. As a result, there is a huge communication barrier between normal and deaf-dumb persons. Again, sign language interpreters are not easily available and it is a very costly solution for a long period. The sign language recognition system reduces the communication gaps between normal and deaf-dumb persons. The methodologies to recognize Indian sign language are recently in the developing stage and there is no approach to recognize signs in real-time. Here, we have proposed a fingerspelling recognition system of static signs for the Indian sign language alphabet using convolutional neural networks combined with data augmentation, batch normalization, dropout, stochastic pooling, and diffGrad optimizer. To continue the research, a total of 62,400 images of 26 static signs have been taken from various users. The proposed method achieves the highest training and validation accuracy of 99.76% and 99.64%, respectively , that outperforms other examined systems.
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allfields	10.1007/s11042-021-11595-4 doi (DE-627)SPR049525476 (SPR)s11042-021-11595-4-e DE-627 ger DE-627 rakwb eng Nandi, Utpal verfasserin (orcid)0000-0002-9638-1906 aut Indian sign language alphabet recognition system using CNN with diffGrad optimizer and stochastic pooling 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract India has the largest deaf population in the world and sign language is the principal medium for such persons to share information with normal people and among themselves. Yet, normal people do not have any knowledge of such language. As a result, there is a huge communication barrier between normal and deaf-dumb persons. Again, sign language interpreters are not easily available and it is a very costly solution for a long period. The sign language recognition system reduces the communication gaps between normal and deaf-dumb persons. The methodologies to recognize Indian sign language are recently in the developing stage and there is no approach to recognize signs in real-time. Here, we have proposed a fingerspelling recognition system of static signs for the Indian sign language alphabet using convolutional neural networks combined with data augmentation, batch normalization, dropout, stochastic pooling, and diffGrad optimizer. To continue the research, a total of 62,400 images of 26 static signs have been taken from various users. The proposed method achieves the highest training and validation accuracy of 99.76% and 99.64%, respectively , that outperforms other examined systems. Sign language (dpeaa)DE-He213 Data augmentation (dpeaa)DE-He213 Pooling (dpeaa)DE-He213 Optimizer (dpeaa)DE-He213 Dropout (dpeaa)DE-He213 Convolution (dpeaa)DE-He213 Batch normalization (dpeaa)DE-He213 Ghorai, Anudyuti aut Singh, Moirangthem Marjit aut Changdar, Chiranjit aut Bhakta, Shubhankar aut Kumar Pal, Rajat aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 82(2022), 7 vom: 08. Jan., Seite 9627-9648 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:82 year:2022 number:7 day:08 month:01 pages:9627-9648 https://dx.doi.org/10.1007/s11042-021-11595-4 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_2190 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2022 7 08 01 9627-9648
spelling	10.1007/s11042-021-11595-4 doi (DE-627)SPR049525476 (SPR)s11042-021-11595-4-e DE-627 ger DE-627 rakwb eng Nandi, Utpal verfasserin (orcid)0000-0002-9638-1906 aut Indian sign language alphabet recognition system using CNN with diffGrad optimizer and stochastic pooling 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract India has the largest deaf population in the world and sign language is the principal medium for such persons to share information with normal people and among themselves. Yet, normal people do not have any knowledge of such language. As a result, there is a huge communication barrier between normal and deaf-dumb persons. Again, sign language interpreters are not easily available and it is a very costly solution for a long period. The sign language recognition system reduces the communication gaps between normal and deaf-dumb persons. The methodologies to recognize Indian sign language are recently in the developing stage and there is no approach to recognize signs in real-time. Here, we have proposed a fingerspelling recognition system of static signs for the Indian sign language alphabet using convolutional neural networks combined with data augmentation, batch normalization, dropout, stochastic pooling, and diffGrad optimizer. To continue the research, a total of 62,400 images of 26 static signs have been taken from various users. The proposed method achieves the highest training and validation accuracy of 99.76% and 99.64%, respectively , that outperforms other examined systems. Sign language (dpeaa)DE-He213 Data augmentation (dpeaa)DE-He213 Pooling (dpeaa)DE-He213 Optimizer (dpeaa)DE-He213 Dropout (dpeaa)DE-He213 Convolution (dpeaa)DE-He213 Batch normalization (dpeaa)DE-He213 Ghorai, Anudyuti aut Singh, Moirangthem Marjit aut Changdar, Chiranjit aut Bhakta, Shubhankar aut Kumar Pal, Rajat aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 82(2022), 7 vom: 08. Jan., Seite 9627-9648 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:82 year:2022 number:7 day:08 month:01 pages:9627-9648 https://dx.doi.org/10.1007/s11042-021-11595-4 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_2190 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2022 7 08 01 9627-9648
allfields_unstemmed	10.1007/s11042-021-11595-4 doi (DE-627)SPR049525476 (SPR)s11042-021-11595-4-e DE-627 ger DE-627 rakwb eng Nandi, Utpal verfasserin (orcid)0000-0002-9638-1906 aut Indian sign language alphabet recognition system using CNN with diffGrad optimizer and stochastic pooling 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract India has the largest deaf population in the world and sign language is the principal medium for such persons to share information with normal people and among themselves. Yet, normal people do not have any knowledge of such language. As a result, there is a huge communication barrier between normal and deaf-dumb persons. Again, sign language interpreters are not easily available and it is a very costly solution for a long period. The sign language recognition system reduces the communication gaps between normal and deaf-dumb persons. The methodologies to recognize Indian sign language are recently in the developing stage and there is no approach to recognize signs in real-time. Here, we have proposed a fingerspelling recognition system of static signs for the Indian sign language alphabet using convolutional neural networks combined with data augmentation, batch normalization, dropout, stochastic pooling, and diffGrad optimizer. To continue the research, a total of 62,400 images of 26 static signs have been taken from various users. The proposed method achieves the highest training and validation accuracy of 99.76% and 99.64%, respectively , that outperforms other examined systems. Sign language (dpeaa)DE-He213 Data augmentation (dpeaa)DE-He213 Pooling (dpeaa)DE-He213 Optimizer (dpeaa)DE-He213 Dropout (dpeaa)DE-He213 Convolution (dpeaa)DE-He213 Batch normalization (dpeaa)DE-He213 Ghorai, Anudyuti aut Singh, Moirangthem Marjit aut Changdar, Chiranjit aut Bhakta, Shubhankar aut Kumar Pal, Rajat aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 82(2022), 7 vom: 08. Jan., Seite 9627-9648 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:82 year:2022 number:7 day:08 month:01 pages:9627-9648 https://dx.doi.org/10.1007/s11042-021-11595-4 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_2190 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2022 7 08 01 9627-9648
allfieldsGer	10.1007/s11042-021-11595-4 doi (DE-627)SPR049525476 (SPR)s11042-021-11595-4-e DE-627 ger DE-627 rakwb eng Nandi, Utpal verfasserin (orcid)0000-0002-9638-1906 aut Indian sign language alphabet recognition system using CNN with diffGrad optimizer and stochastic pooling 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract India has the largest deaf population in the world and sign language is the principal medium for such persons to share information with normal people and among themselves. Yet, normal people do not have any knowledge of such language. As a result, there is a huge communication barrier between normal and deaf-dumb persons. Again, sign language interpreters are not easily available and it is a very costly solution for a long period. The sign language recognition system reduces the communication gaps between normal and deaf-dumb persons. The methodologies to recognize Indian sign language are recently in the developing stage and there is no approach to recognize signs in real-time. Here, we have proposed a fingerspelling recognition system of static signs for the Indian sign language alphabet using convolutional neural networks combined with data augmentation, batch normalization, dropout, stochastic pooling, and diffGrad optimizer. To continue the research, a total of 62,400 images of 26 static signs have been taken from various users. The proposed method achieves the highest training and validation accuracy of 99.76% and 99.64%, respectively , that outperforms other examined systems. Sign language (dpeaa)DE-He213 Data augmentation (dpeaa)DE-He213 Pooling (dpeaa)DE-He213 Optimizer (dpeaa)DE-He213 Dropout (dpeaa)DE-He213 Convolution (dpeaa)DE-He213 Batch normalization (dpeaa)DE-He213 Ghorai, Anudyuti aut Singh, Moirangthem Marjit aut Changdar, Chiranjit aut Bhakta, Shubhankar aut Kumar Pal, Rajat aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 82(2022), 7 vom: 08. Jan., Seite 9627-9648 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:82 year:2022 number:7 day:08 month:01 pages:9627-9648 https://dx.doi.org/10.1007/s11042-021-11595-4 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_2190 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2022 7 08 01 9627-9648
allfieldsSound	10.1007/s11042-021-11595-4 doi (DE-627)SPR049525476 (SPR)s11042-021-11595-4-e DE-627 ger DE-627 rakwb eng Nandi, Utpal verfasserin (orcid)0000-0002-9638-1906 aut Indian sign language alphabet recognition system using CNN with diffGrad optimizer and stochastic pooling 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract India has the largest deaf population in the world and sign language is the principal medium for such persons to share information with normal people and among themselves. Yet, normal people do not have any knowledge of such language. As a result, there is a huge communication barrier between normal and deaf-dumb persons. Again, sign language interpreters are not easily available and it is a very costly solution for a long period. The sign language recognition system reduces the communication gaps between normal and deaf-dumb persons. The methodologies to recognize Indian sign language are recently in the developing stage and there is no approach to recognize signs in real-time. Here, we have proposed a fingerspelling recognition system of static signs for the Indian sign language alphabet using convolutional neural networks combined with data augmentation, batch normalization, dropout, stochastic pooling, and diffGrad optimizer. To continue the research, a total of 62,400 images of 26 static signs have been taken from various users. The proposed method achieves the highest training and validation accuracy of 99.76% and 99.64%, respectively , that outperforms other examined systems. Sign language (dpeaa)DE-He213 Data augmentation (dpeaa)DE-He213 Pooling (dpeaa)DE-He213 Optimizer (dpeaa)DE-He213 Dropout (dpeaa)DE-He213 Convolution (dpeaa)DE-He213 Batch normalization (dpeaa)DE-He213 Ghorai, Anudyuti aut Singh, Moirangthem Marjit aut Changdar, Chiranjit aut Bhakta, Shubhankar aut Kumar Pal, Rajat aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 82(2022), 7 vom: 08. Jan., Seite 9627-9648 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:82 year:2022 number:7 day:08 month:01 pages:9627-9648 https://dx.doi.org/10.1007/s11042-021-11595-4 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_2190 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2022 7 08 01 9627-9648
language	English
source	Enthalten in Multimedia tools and applications 82(2022), 7 vom: 08. Jan., Seite 9627-9648 volume:82 year:2022 number:7 day:08 month:01 pages:9627-9648
sourceStr	Enthalten in Multimedia tools and applications 82(2022), 7 vom: 08. Jan., Seite 9627-9648 volume:82 year:2022 number:7 day:08 month:01 pages:9627-9648
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Sign language Data augmentation Pooling Optimizer Dropout Convolution Batch normalization
isfreeaccess_bool	false
container_title	Multimedia tools and applications
authorswithroles_txt_mv	Nandi, Utpal @@aut@@ Ghorai, Anudyuti @@aut@@ Singh, Moirangthem Marjit @@aut@@ Changdar, Chiranjit @@aut@@ Bhakta, Shubhankar @@aut@@ Kumar Pal, Rajat @@aut@@
publishDateDaySort_date	2022-01-08T00:00:00Z
hierarchy_top_id	27135030X
id	SPR049525476
language_de	englisch
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author	Nandi, Utpal
spellingShingle	Nandi, Utpal misc Sign language misc Data augmentation misc Pooling misc Optimizer misc Dropout misc Convolution misc Batch normalization Indian sign language alphabet recognition system using CNN with diffGrad optimizer and stochastic pooling
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topic_title	Indian sign language alphabet recognition system using CNN with diffGrad optimizer and stochastic pooling Sign language (dpeaa)DE-He213 Data augmentation (dpeaa)DE-He213 Pooling (dpeaa)DE-He213 Optimizer (dpeaa)DE-He213 Dropout (dpeaa)DE-He213 Convolution (dpeaa)DE-He213 Batch normalization (dpeaa)DE-He213
topic	misc Sign language misc Data augmentation misc Pooling misc Optimizer misc Dropout misc Convolution misc Batch normalization
topic_unstemmed	misc Sign language misc Data augmentation misc Pooling misc Optimizer misc Dropout misc Convolution misc Batch normalization
topic_browse	misc Sign language misc Data augmentation misc Pooling misc Optimizer misc Dropout misc Convolution misc Batch normalization
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
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title	Indian sign language alphabet recognition system using CNN with diffGrad optimizer and stochastic pooling
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title_full	Indian sign language alphabet recognition system using CNN with diffGrad optimizer and stochastic pooling
author_sort	Nandi, Utpal
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author_browse	Nandi, Utpal Ghorai, Anudyuti Singh, Moirangthem Marjit Changdar, Chiranjit Bhakta, Shubhankar Kumar Pal, Rajat
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title_sort	indian sign language alphabet recognition system using cnn with diffgrad optimizer and stochastic pooling
title_auth	Indian sign language alphabet recognition system using CNN with diffGrad optimizer and stochastic pooling
abstract	Abstract India has the largest deaf population in the world and sign language is the principal medium for such persons to share information with normal people and among themselves. Yet, normal people do not have any knowledge of such language. As a result, there is a huge communication barrier between normal and deaf-dumb persons. Again, sign language interpreters are not easily available and it is a very costly solution for a long period. The sign language recognition system reduces the communication gaps between normal and deaf-dumb persons. The methodologies to recognize Indian sign language are recently in the developing stage and there is no approach to recognize signs in real-time. Here, we have proposed a fingerspelling recognition system of static signs for the Indian sign language alphabet using convolutional neural networks combined with data augmentation, batch normalization, dropout, stochastic pooling, and diffGrad optimizer. To continue the research, a total of 62,400 images of 26 static signs have been taken from various users. The proposed method achieves the highest training and validation accuracy of 99.76% and 99.64%, respectively , that outperforms other examined systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
abstractGer	Abstract India has the largest deaf population in the world and sign language is the principal medium for such persons to share information with normal people and among themselves. Yet, normal people do not have any knowledge of such language. As a result, there is a huge communication barrier between normal and deaf-dumb persons. Again, sign language interpreters are not easily available and it is a very costly solution for a long period. The sign language recognition system reduces the communication gaps between normal and deaf-dumb persons. The methodologies to recognize Indian sign language are recently in the developing stage and there is no approach to recognize signs in real-time. Here, we have proposed a fingerspelling recognition system of static signs for the Indian sign language alphabet using convolutional neural networks combined with data augmentation, batch normalization, dropout, stochastic pooling, and diffGrad optimizer. To continue the research, a total of 62,400 images of 26 static signs have been taken from various users. The proposed method achieves the highest training and validation accuracy of 99.76% and 99.64%, respectively , that outperforms other examined systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
abstract_unstemmed	Abstract India has the largest deaf population in the world and sign language is the principal medium for such persons to share information with normal people and among themselves. Yet, normal people do not have any knowledge of such language. As a result, there is a huge communication barrier between normal and deaf-dumb persons. Again, sign language interpreters are not easily available and it is a very costly solution for a long period. The sign language recognition system reduces the communication gaps between normal and deaf-dumb persons. The methodologies to recognize Indian sign language are recently in the developing stage and there is no approach to recognize signs in real-time. Here, we have proposed a fingerspelling recognition system of static signs for the Indian sign language alphabet using convolutional neural networks combined with data augmentation, batch normalization, dropout, stochastic pooling, and diffGrad optimizer. To continue the research, a total of 62,400 images of 26 static signs have been taken from various users. The proposed method achieves the highest training and validation accuracy of 99.76% and 99.64%, respectively , that outperforms other examined systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
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title_short	Indian sign language alphabet recognition system using CNN with diffGrad optimizer and stochastic pooling
url	https://dx.doi.org/10.1007/s11042-021-11595-4
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author2	Ghorai, Anudyuti Singh, Moirangthem Marjit Changdar, Chiranjit Bhakta, Shubhankar Kumar Pal, Rajat
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up_date	2024-07-04T01:10:40.719Z
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Yet, normal people do not have any knowledge of such language. As a result, there is a huge communication barrier between normal and deaf-dumb persons. Again, sign language interpreters are not easily available and it is a very costly solution for a long period. The sign language recognition system reduces the communication gaps between normal and deaf-dumb persons. The methodologies to recognize Indian sign language are recently in the developing stage and there is no approach to recognize signs in real-time. Here, we have proposed a fingerspelling recognition system of static signs for the Indian sign language alphabet using convolutional neural networks combined with data augmentation, batch normalization, dropout, stochastic pooling, and diffGrad optimizer. To continue the research, a total of 62,400 images of 26 static signs have been taken from various users. 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Indian sign language alphabet recognition system using CNN with diffGrad optimizer and stochastic pooling

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