| Presentation | 2022-03-08 A Study on Sign Recognition Using Deep Learning Hikaru Isogai, Tsutomu Kimura, Kanda Kazuyuki, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In this study, our purpose is to recognize signs using machine learning. In order to take into account the transition motions that occur in a sign sentence, machine learning adopts the sign sentences as training data, and a trained model is created. We experimented two models: one that incorporates Connectionist Temporal Classification (CTC) which is a method used in speech recognition, and the other is a conformer model that uses a transformer used in natural language processing. As the result, the recognition rate for the entire test data was about 74% by the CTC method and about 32% by the Conformer method. However, the recognition results of the Conformer method showed a phenomenon as over-learning, and we estimated that it might worked properly. We will improve the Conformer method and will investigate a new algorithm that combines the Transformer with CTC. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Sign Recognition / Deep Learning / Connectionist Temporal Classification / Transformer / Conformer |
| Paper # | WIT2021-48 |
| Date of Issue | 2022-03-01 (WIT) |
| Conference Information | |
| Committee | WIT / IPSJ-AAC |
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| Conference Date | 2022/3/8(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Shinji Sakou(Nagoya Inst. of Tech.) |
| Vice Chair | Tomohiro Amemiya(Univ. of Tokyo) |
| Secretary | Tomohiro Amemiya(Saitama Industrial Tech. Center) / (Teikyo Univ.) |
| Assistant | Minako Hosono(AIST) / Aki Sugano(Nagoya Univ.) / Tomoyasu Komori(NHK) |
| Paper Information | |
| Registration To | Technical Committee on Well-being Information Technology / Special Interest Group on Assistive & Accessible Computin |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Study on Sign Recognition Using Deep Learning |
| Sub Title (in English) | Comparison between CTC and Conformer |
| Keyword(1) | Sign Recognition |
| Keyword(2) | Deep Learning |
| Keyword(3) | Connectionist Temporal Classification |
| Keyword(4) | Transformer |
| Keyword(5) | Conformer |
| 1st Author's Name | Hikaru Isogai |
| 1st Author's Affiliation | National Institute of Technology, Toyota College(NIT, Toyota College) |
| 2nd Author's Name | Tsutomu Kimura |
| 2nd Author's Affiliation | National Institute of Technology, Toyota College(NIT, Toyota College) |
| 3rd Author's Name | Kanda Kazuyuki |
| 3rd Author's Affiliation | National Museum of Ethnology(National Museum of Ethnology) |
| Date | 2022-03-08 |
| Paper # | WIT2021-48 |
| Volume (vol) | vol.121 |
| Number (no) | WIT-418 |
| Page | pp.pp.29-34(WIT), |
| #Pages | 6 |
| Date of Issue | 2022-03-01 (WIT) |