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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.10975 |
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| _version_ | 1866913432053219328 |
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| author | Ma, Jiyong Gao, Wen Wang, Chunli |
| author_facet | Ma, Jiyong Gao, Wen Wang, Chunli |
| contents | In this paper, a novel approach to sign language recognition based on state tying in each of data streams is presented. In this framework, it is assumed that hand gesture signal is represented in terms of six synchronous data streams, i.e., the left/right hand position, left/right hand orientation and left/right handshape. This approach offers a very accurate representation of the sign space and keeps the number of parameters reasonably small in favor of a fast decoding. Experiments were carried out for 5177 Chinese signs. The real time isolated recognition rate is 94.8%. For continuous sign recognition, the word correct rate is 91.4%. Keywords: Sign language recognition; Automatic sign language translation; Hand gesture recognition; Hidden Markov models; State-tying; Multimodal user interface; Virtual reality; Man-machine systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_10975 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Stream State-tying for Sign Language Recognition Ma, Jiyong Gao, Wen Wang, Chunli Other Computer Science Artificial Intelligence Computation and Language In this paper, a novel approach to sign language recognition based on state tying in each of data streams is presented. In this framework, it is assumed that hand gesture signal is represented in terms of six synchronous data streams, i.e., the left/right hand position, left/right hand orientation and left/right handshape. This approach offers a very accurate representation of the sign space and keeps the number of parameters reasonably small in favor of a fast decoding. Experiments were carried out for 5177 Chinese signs. The real time isolated recognition rate is 94.8%. For continuous sign recognition, the word correct rate is 91.4%. Keywords: Sign language recognition; Automatic sign language translation; Hand gesture recognition; Hidden Markov models; State-tying; Multimodal user interface; Virtual reality; Man-machine systems. |
| title | Stream State-tying for Sign Language Recognition |
| topic | Other Computer Science Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2407.10975 |