| Research Title |
Thai finger spelling localization and classification under complex background using a YOLO-based deep learning |
| Date of Distribution |
19 January 2019 |
| Conference |
| Title of the Conference |
The 11th International Conference on Computer Modeling and Simulation (ICCMS 2019) and The 8th International Conference on Intelligent Computing and Applications (ICICA 2019) |
| Organiser |
International Association of Computer Science and Information Technology (IACSIT) |
| Conference Place |
Melbourne |
| Province/State |
Victoria Australia |
| Conference Date |
16 January 2019 |
| To |
19 January 2019 |
| Proceeding Paper |
| Volume |
2019 |
| Issue |
1 |
| Page |
- |
| Editors/edition/publisher |
ACM's International Conference Proceedings Series (ICPS) |
| Abstract |
Sign language recognition has been actively studied and remains a challenge in computer vision. The finger spelling is an integral part of a sign language. This study focuses on Thai finger spelling(TFS), especially TFS single hand schema under complex background condition. We proposed a YOLO-based Thai finger spelling(Y-TFS) that used the convolution neural network architecture to localize and classify 25 TFS signs. The experiment on the training dataset of 15,000 images and test dataset of 15,000 images shows that our system has performed well and is robust against various background conditions. For the Thai fingerspelling recognition, our Y-TFS achieved the mAPs of 82.06% under a complex background and 84.99 % under a plain background. |
| Author |
|
| Peer Review Status |
มีผู้ประเมินอิสระ |
| Level of Conference |
นานาชาติ |
| Type of Proceeding |
Full paper |
| Type of Presentation |
Oral |
| Part of thesis |
false |
| Presentation awarding |
false |
| Attach file |
|
| Citation |
0
|
|
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Publication
Journal Publication
ไทย