Real Time American Sign Language Detection Using Yolo-v9
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909267487883264 |
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| author | Imran, Amna Hulikal, Meghana Shashishekhara Gardi, Hamza A. A. |
| author_facet | Imran, Amna Hulikal, Meghana Shashishekhara Gardi, Hamza A. A. |
| contents | This paper focuses on real-time American Sign Language Detection. YOLO is a convolutional neural network (CNN) based model, which was first released in 2015. In recent years, it gained popularity for its real-time detection capabilities. Our study specifically targets YOLO-v9 model, released in 2024. As the model is newly introduced, not much work has been done on it, especially not in Sign Language Detection. Our paper provides deep insight on how YOLO- v9 works and better than previous model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_17950 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Real Time American Sign Language Detection Using Yolo-v9 Imran, Amna Hulikal, Meghana Shashishekhara Gardi, Hamza A. A. Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning This paper focuses on real-time American Sign Language Detection. YOLO is a convolutional neural network (CNN) based model, which was first released in 2015. In recent years, it gained popularity for its real-time detection capabilities. Our study specifically targets YOLO-v9 model, released in 2024. As the model is newly introduced, not much work has been done on it, especially not in Sign Language Detection. Our paper provides deep insight on how YOLO- v9 works and better than previous model. |
| title | Real Time American Sign Language Detection Using Yolo-v9 |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2407.17950 |