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.
|
Pdf
CB20179.pdf - Accepted Version Download (3MB) | Preview |
Abstract
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.
| Item Type: | Undergraduates Project Papers |
|---|---|
| Additional Information: | SV: Dr. Zuriani Binti Mustaffa |
| Uncontrolled Keywords: | Malay Sign Language (MSL), object detection model, Kaggle |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Faculty/Division: | Faculty of Computing |
| Depositing User: | Mr. Nik Ahmad Nasyrun Nik Abd Malik |
| Date Deposited: | 04 Apr 2024 06:31 |
| Last Modified: | 04 Apr 2024 06:31 |
| URI: | http://umpir.ump.edu.my/id/eprint/40906 |
| Download Statistic: | View Download Statistics |
Actions (login required)
 |
View Item |