Deep Learning Approaches for Age-based Gesture Classification in South Indian Sign Language
This study focuses on recognizing and categorizing South Indian Sign Language gestures based on different age groups through transfer learning models. Sign language serves as a natural and expressive communication method for individuals with hearing impairments. This study intends to develop deep tr...
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| Format: | Article |
| Language: | English |
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D. G. Pylarinos
2024-04-01
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| Series: | Engineering, Technology & Applied Science Research |
| Subjects: | |
| Online Access: | https://etasr.com/index.php/ETASR/article/view/6864 |
| _version_ | 1797226320802873344 |
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| author | Ramesh M. Badiger Rajesh Yakkundimath Guruprasad Konnurmath Praveen M. Dhulavvagol |
| author_facet | Ramesh M. Badiger Rajesh Yakkundimath Guruprasad Konnurmath Praveen M. Dhulavvagol |
| author_sort | Ramesh M. Badiger |
| collection | DOAJ |
| description | This study focuses on recognizing and categorizing South Indian Sign Language gestures based on different age groups through transfer learning models. Sign language serves as a natural and expressive communication method for individuals with hearing impairments. This study intends to develop deep transfer learning models, namely Inception-V3, VGG-16, and ResNet-50, to accurately identify and classify double-handed gestures in South Indian languages, like Kannada, Tamil, and Telugu. A dataset comprising 30,000 images of double-handed gestures, with 10,000 images for each considered age group (1-7, 8-25, and 25 and above), is utilized to enhance and modify the models for improved classification performance. Amongst the tested models, Inception-V3 achieves the best performance with a test precision of 95.20% and validation accuracy of 92.45%, demonstrating its effectiveness in accurately categorizing images of double-handed gestures into ten different classes.
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| first_indexed | 2024-04-24T14:23:02Z |
| format | Article |
| id | doaj.art-e71226da4de146c6a04a293c24037492 |
| institution | Directory Open Access Journal |
| issn | 2241-4487 1792-8036 |
| language | English |
| last_indexed | 2024-04-24T14:23:02Z |
| publishDate | 2024-04-01 |
| publisher | D. G. Pylarinos |
| record_format | Article |
| series | Engineering, Technology & Applied Science Research |
| spelling | doaj.art-e71226da4de146c6a04a293c240374922024-04-03T06:14:14ZengD. G. PylarinosEngineering, Technology & Applied Science Research2241-44871792-80362024-04-0114210.48084/etasr.6864Deep Learning Approaches for Age-based Gesture Classification in South Indian Sign LanguageRamesh M. Badiger0Rajesh Yakkundimath1Guruprasad Konnurmath2Praveen M. Dhulavvagol3Department of Computer Science and Engineering, Tontadarya College of Engineering, IndiaDepartment of Computer Science and Engineering, KLE Institute of Technology, IndiaSchool of Computer Science and Engineering, KLE Technological University, Hubballi, IndiaSchool of Computer Science and Engineering, KLE Technological University, Hubballi, IndiaThis study focuses on recognizing and categorizing South Indian Sign Language gestures based on different age groups through transfer learning models. Sign language serves as a natural and expressive communication method for individuals with hearing impairments. This study intends to develop deep transfer learning models, namely Inception-V3, VGG-16, and ResNet-50, to accurately identify and classify double-handed gestures in South Indian languages, like Kannada, Tamil, and Telugu. A dataset comprising 30,000 images of double-handed gestures, with 10,000 images for each considered age group (1-7, 8-25, and 25 and above), is utilized to enhance and modify the models for improved classification performance. Amongst the tested models, Inception-V3 achieves the best performance with a test precision of 95.20% and validation accuracy of 92.45%, demonstrating its effectiveness in accurately categorizing images of double-handed gestures into ten different classes. https://etasr.com/index.php/ETASR/article/view/6864sign languageage groupgesture identificationtransfer learningInception-V3VGG-16 |
| spellingShingle | Ramesh M. Badiger Rajesh Yakkundimath Guruprasad Konnurmath Praveen M. Dhulavvagol Deep Learning Approaches for Age-based Gesture Classification in South Indian Sign Language Engineering, Technology & Applied Science Research sign language age group gesture identification transfer learning Inception-V3 VGG-16 |
| title | Deep Learning Approaches for Age-based Gesture Classification in South Indian Sign Language |
| title_full | Deep Learning Approaches for Age-based Gesture Classification in South Indian Sign Language |
| title_fullStr | Deep Learning Approaches for Age-based Gesture Classification in South Indian Sign Language |
| title_full_unstemmed | Deep Learning Approaches for Age-based Gesture Classification in South Indian Sign Language |
| title_short | Deep Learning Approaches for Age-based Gesture Classification in South Indian Sign Language |
| title_sort | deep learning approaches for age based gesture classification in south indian sign language |
| topic | sign language age group gesture identification transfer learning Inception-V3 VGG-16 |
| url | https://etasr.com/index.php/ETASR/article/view/6864 |
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