Learning Sign Language Using Single Shot Detector (SSD) And Mobilenet
Sign languages are a form of communication used by the deaf and hard-of-hearing community. Malay Sign Language (MSL) is the official sign language that is practiced in Malaysia to communicate using hand signs and facial expressions. Every sign and its combination have a different meaning, this makes...
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| Materyal Türü: | Undergraduates Project Papers |
| Dil: | English |
| Baskı/Yayın Bilgisi: |
2023
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| Konular: | |
| Online Erişim: | http://umpir.ump.edu.my/id/eprint/40906/1/CB20179.pdf |
| _version_ | 1825815581528424448 |
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| author | Nik Ahmad Farihin, Mohd Zulkifli |
| author_facet | Nik Ahmad Farihin, Mohd Zulkifli |
| author_sort | Nik Ahmad Farihin, Mohd Zulkifli |
| collection | UMP |
| description | Sign languages are a form of communication used by the deaf and hard-of-hearing community. Malay Sign Language (MSL) is the official sign language that is practiced in Malaysia to communicate using hand signs and facial expressions. Every sign and its combination have a different meaning, this makes it quite hard for people to just casually pick up Malay Sign Language to learn. Therefore, this study presents an object detection model using Single Shot Detector (SSD) and Mobilenet to detect Sign Language in real time. This model is only trained to detect static signs which didn’t require any complex combination. The dataset consists of 2000 sign images that were collected from a website called Kaggle and collected using a personal camera. For the training, validation, and testing phases, the dataset was divided into 8:1:1 respectively. In conclusion, this thesis has succeeded in developing a real-time and accurate system for MSL recognition using the SSD-Mobilenet model, which can contribute to the field of sign language recognition and help to improve communication access for deaf and hard-of-hearing individuals. |
| first_indexed | 2024-04-09T03:52:37Z |
| format | Undergraduates Project Papers |
| id | UMPir40906 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-04-09T03:52:37Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | UMPir409062024-04-04T06:31:35Z http://umpir.ump.edu.my/id/eprint/40906/ Learning Sign Language Using Single Shot Detector (SSD) And Mobilenet Nik Ahmad Farihin, Mohd Zulkifli QA75 Electronic computers. Computer science Sign languages are a form of communication used by the deaf and hard-of-hearing community. Malay Sign Language (MSL) is the official sign language that is practiced in Malaysia to communicate using hand signs and facial expressions. Every sign and its combination have a different meaning, this makes it quite hard for people to just casually pick up Malay Sign Language to learn. Therefore, this study presents an object detection model using Single Shot Detector (SSD) and Mobilenet to detect Sign Language in real time. This model is only trained to detect static signs which didn’t require any complex combination. The dataset consists of 2000 sign images that were collected from a website called Kaggle and collected using a personal camera. For the training, validation, and testing phases, the dataset was divided into 8:1:1 respectively. In conclusion, this thesis has succeeded in developing a real-time and accurate system for MSL recognition using the SSD-Mobilenet model, which can contribute to the field of sign language recognition and help to improve communication access for deaf and hard-of-hearing individuals. 2023-07 Undergraduates Project Papers NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40906/1/CB20179.pdf Nik Ahmad Farihin, Mohd Zulkifli (2023) Learning Sign Language Using Single Shot Detector (SSD) And Mobilenet. Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah. |
| spellingShingle | QA75 Electronic computers. Computer science Nik Ahmad Farihin, Mohd Zulkifli Learning Sign Language Using Single Shot Detector (SSD) And Mobilenet |
| title | Learning Sign Language Using Single Shot Detector (SSD) And Mobilenet |
| title_full | Learning Sign Language Using Single Shot Detector (SSD) And Mobilenet |
| title_fullStr | Learning Sign Language Using Single Shot Detector (SSD) And Mobilenet |
| title_full_unstemmed | Learning Sign Language Using Single Shot Detector (SSD) And Mobilenet |
| title_short | Learning Sign Language Using Single Shot Detector (SSD) And Mobilenet |
| title_sort | learning sign language using single shot detector ssd and mobilenet |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/40906/1/CB20179.pdf |
| work_keys_str_mv | AT nikahmadfarihinmohdzulkifli learningsignlanguageusingsingleshotdetectorssdandmobilenet |