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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| Online Access: | http://eprints.utar.edu.my/6654/ http://eprints.utar.edu.my/6654/1/fyp_CS_2024_LTJ.pdf |
| _version_ | 1848886737507450880 |
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| author | Lee, Teck Junn |
| author_facet | Lee, Teck Junn |
| author_sort | Lee, Teck Junn |
| building | UTAR Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T19:43:15Z |
| format | Final Year Project / Dissertation / Thesis |
| id | utar-6654 |
| institution | Universiti Tunku Abdul Rahman |
| institution_category | Local University |
| last_indexed | 2025-11-15T19:43:15Z |
| publishDate | 2024 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utar-66542024-10-23T06:01:22Z Automated hand gesture recognition for enhancing sign language communication Lee, Teck Junn L Education (General) T Technology (General) TD Environmental technology. Sanitary engineering 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/6654/1/fyp_CS_2024_LTJ.pdf Lee, Teck Junn (2024) Automated hand gesture recognition for enhancing sign language communication. Final Year Project, UTAR. http://eprints.utar.edu.my/6654/ |
| spellingShingle | L Education (General) T Technology (General) TD Environmental technology. Sanitary engineering Lee, Teck Junn Automated hand gesture recognition for enhancing sign language communication |
| title | Automated hand gesture recognition for enhancing sign language communication |
| title_full | Automated hand gesture recognition for enhancing sign language communication |
| title_fullStr | Automated hand gesture recognition for enhancing sign language communication |
| title_full_unstemmed | Automated hand gesture recognition for enhancing sign language communication |
| title_short | Automated hand gesture recognition for enhancing sign language communication |
| title_sort | automated hand gesture recognition for enhancing sign language communication |
| topic | L Education (General) T Technology (General) TD Environmental technology. Sanitary engineering |
| url | http://eprints.utar.edu.my/6654/ http://eprints.utar.edu.my/6654/1/fyp_CS_2024_LTJ.pdf |