Automatic sign language detector for video call
Video conference has been a big part of our lives since COVID-19 hit but the hearing-impaired does not have the ability to communicate in an efficient way when video conferencing. Singapore Association For The Deaf (SADeaf) state that there was a rise of interest to learn sign language for commun...
| Main Author: | |
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/148038 |
| _version_ | 1826124076594233344 |
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| author | Chua, Mark De Wen |
| author2 | Lam Siew Kei |
| author_facet | Lam Siew Kei Chua, Mark De Wen |
| author_sort | Chua, Mark De Wen |
| collection | NTU |
| description | Video conference has been a big part of our lives since COVID-19 hit but the
hearing-impaired does not have the ability to communicate in an efficient way when
video conferencing. Singapore Association For The Deaf (SADeaf) state that there was a
rise of interest to learn sign language for communication with hearing-impaired family
member or co-workers. However, there is a steep learning curve for learning sign
language. This project aims to allow real-time interpretation of sign language using
You-Only-Look-Once(YOLO) neural networks. The application will be designed to
output the word visually and audibly when the user uses sign language on their web
camera while video conferencing. |
| first_indexed | 2024-10-01T06:14:45Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/148038 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:14:45Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1480382021-04-22T06:15:38Z Automatic sign language detector for video call Chua, Mark De Wen Lam Siew Kei School of Computer Science and Engineering ASSKLam@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Engineering::Computer science and engineering::Computing methodologies::Pattern recognition Video conference has been a big part of our lives since COVID-19 hit but the hearing-impaired does not have the ability to communicate in an efficient way when video conferencing. Singapore Association For The Deaf (SADeaf) state that there was a rise of interest to learn sign language for communication with hearing-impaired family member or co-workers. However, there is a steep learning curve for learning sign language. This project aims to allow real-time interpretation of sign language using You-Only-Look-Once(YOLO) neural networks. The application will be designed to output the word visually and audibly when the user uses sign language on their web camera while video conferencing. Bachelor of Engineering (Computer Engineering) 2021-04-22T06:15:38Z 2021-04-22T06:15:38Z 2021 Final Year Project (FYP) Chua, M. D. W. (2021). Automatic sign language detector for video call. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148038 https://hdl.handle.net/10356/148038 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Engineering::Computer science and engineering::Computing methodologies::Pattern recognition Chua, Mark De Wen Automatic sign language detector for video call |
| title | Automatic sign language detector for video call |
| title_full | Automatic sign language detector for video call |
| title_fullStr | Automatic sign language detector for video call |
| title_full_unstemmed | Automatic sign language detector for video call |
| title_short | Automatic sign language detector for video call |
| title_sort | automatic sign language detector for video call |
| topic | Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Engineering::Computer science and engineering::Computing methodologies::Pattern recognition |
| url | https://hdl.handle.net/10356/148038 |
| work_keys_str_mv | AT chuamarkdewen automaticsignlanguagedetectorforvideocall |