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Publication
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| Title of Article |
Thai Finger-Spelling Sign Language Recognition Employing PHOG and Local Features with KNN
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| Date of Acceptance |
16 April 2019 |
| Journal |
| Title of Journal |
International Journal of Advances in Soft Computing and its Application |
| Standard |
SCOPUS |
| Institute of Journal |
Al-Zaytoonah University of Jordan |
| ISBN/ISSN |
ISSN: 2074-8523 |
| Volume |
11 |
| Issue |
3 |
| Month |
November |
| Year of Publication |
2019 |
| Page |
94-107 |
| Abstract |
Sign Language recognition is an important tool for the hearing-impaired in order to communicate with both hearing-impaired and hearing individuals.
Similarities in finger-spelling sign language
are one of the main factors or problems influencing the accuracy of sign language recognition. This research focuses on one-stroke, Thai finger-spelling sign language (TFSL) in methods of feature extraction with the pyramid histogram of oriented gradients (PHOG) and local features, as well as the application of K-Nearest Neighbors
(KNN) recognition. We present, herein, a Thai finger-spelling sign language recognition system (TFSLR) used to classify alphabets shown in similar gestures. Five signers postured fifteen Thai alphabet characters, in which images were taken in five repetitions, totaling 375 finger-spelling images. Our experiment utilizes five-fold cross-validation in order to evaluate the projected system effectiveness. Additionally, we compared the results of each experiment involving PHOG with the amalgamation of PHOG and the local features. The results showed that such amalgamation was
capable of handling similarly signed characters with an average accuracy of 97.6%.
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| Keyword |
Finger-spelling sign language, histogram of oriented gradients, k-nearest neighbors, PHOG. |
| Author |
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| Reviewing Status |
มีผู้ประเมินอิสระ |
| Status |
ตีพิมพ์แล้ว |
| Level of Publication |
นานาชาติ |
| citation |
true |
| Part of thesis |
true |
| ใช้สำหรับสำเร็จการศึกษา |
ไม่เป็น |
| Attach file |
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| Citation |
0
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Journal Publication
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