Automated hand gesture recognition for enhancing sign language communication
This paper introduces a novel approach aimed at enhancing communication between individuals who are deaf or hard of hearing and those unfamiliar with sign language. The project addresses this challenge by developing a mobile application that harnesses the power of smartphone cameras, coupled with a...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2024
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| Subjects: | |
| Online Access: | http://eprints.utar.edu.my/6525/1/20ACB02030_FYP.pdf http://eprints.utar.edu.my/6525/ |
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| _version_ | 1850177513422061568 |
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| author | Lee, Teck Junn |
| author_facet | Lee, Teck Junn |
| author_sort | Lee, Teck Junn |
| building | UTAR Library |
| collection | Institutional Repository |
| content_provider | Universiti Tunku Abdul Rahman |
| content_source | UTAR Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This paper introduces a novel approach aimed at enhancing communication between individuals who are deaf or hard of hearing and those unfamiliar with sign language. The project addresses this challenge by developing a mobile application that harnesses the power of smartphone cameras, coupled with a deep learning model, to interpret hand gestures and provide real-time contextual information to users. It emphasizes the widespread adoption of smartphones and the practical applicability of mobile applications in real-life scenarios. Furthermore, the paper proposes a new methodology leveraging Google’s MediaPipe, which outperforms traditional approaches such as transfer learning with pre-trained object detection models in deep learning model development. Of paramount importance is the seamless integration of the deep learning model with the mobile application, enabling real-time detection and recognition on the mobile application. |
| format | Final Year Project / Dissertation / Thesis |
| id | my-utar-eprints.6525 |
| institution | Universiti Tunku Abdul Rahman |
| publishDate | 2024 |
| record_format | eprints |
| spelling | my-utar-eprints.65252025-11-13T10:51:56Z Automated hand gesture recognition for enhancing sign language communication Lee, Teck Junn T Technology (General) This paper introduces a novel approach aimed at enhancing communication between individuals who are deaf or hard of hearing and those unfamiliar with sign language. The project addresses this challenge by developing a mobile application that harnesses the power of smartphone cameras, coupled with a deep learning model, to interpret hand gestures and provide real-time contextual information to users. It emphasizes the widespread adoption of smartphones and the practical applicability of mobile applications in real-life scenarios. Furthermore, the paper proposes a new methodology leveraging Google’s MediaPipe, which outperforms traditional approaches such as transfer learning with pre-trained object detection models in deep learning model development. Of paramount importance is the seamless integration of the deep learning model with the mobile application, enabling real-time detection and recognition on the mobile application. 2024-01 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6525/1/20ACB02030_FYP.pdf Lee, Teck Junn (2024) Automated hand gesture recognition for enhancing sign language communication. Final Year Project, UTAR. http://eprints.utar.edu.my/6525/ |
| spellingShingle | T Technology (General) Lee, Teck Junn Automated hand gesture recognition for enhancing sign language communication |
| title | Automated hand gesture recognition for enhancing sign language communication
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| title_full | Automated hand gesture recognition for enhancing sign language communication
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| title_fullStr | Automated hand gesture recognition for enhancing sign language communication
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| title_full_unstemmed | Automated hand gesture recognition for enhancing sign language communication
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| title_short | Automated hand gesture recognition for enhancing sign language communication
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| title_sort | automated hand gesture recognition for enhancing sign language communication |
| topic | T Technology (General) |
| url | http://eprints.utar.edu.my/6525/1/20ACB02030_FYP.pdf http://eprints.utar.edu.my/6525/ |
| url_provider | http://eprints.utar.edu.my |