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| Presentation | 2021-03-06 14:40 A Study on Sign Language Recognition Using Deep Learning -- Word recognition from sign language sentences -- Isogai Hikaru, Kimura Tsutomu (NIT, Toyota College), Kanda Kazuyuki (National Museum of Ethnology)  WIT2020-38 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In this study, we developed a machine leaning-based sign language recognition system that can recognize each word in a sign language sentence. In our previous study, we developed a learning model using videos of sign language words as training data and obtained a recognition rate of about 90% for words of Sign Language Proficiency Test Grade 6. In addition, we used this training data to recognize sign language sentences using the "Connectionist Temporal Classification" method. However, we found that the recognition rate of words in the sign language sentences decreased because the Home Position was included in the data. Therefore, we attempted to solve this problem by using sign language sentences as training data. As a result, the recognition rate was improved, and we found that the recognition rate improved by increasing the number of sign language sentences used for training. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | sign language / deep learning / Connectionist Temporal Classification / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 419, WIT2020-38, pp. 47-52, March 2021. | |
| Paper # | WIT2020-38 | |
| Date of Issue | 2021-02-26 (WIT) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | WIT2020-38 |
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| Conference Information | |
| Committee | WIT IPSJ-AAC |
| Conference Date | 2021-03-05 - 2021-03-06 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | WIT |
| Conference Code | 2021-03-WIT-AAC |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Study on Sign Language Recognition Using Deep Learning |
| Sub Title (in English) | Word recognition from sign language sentences |
| Keyword(1) | sign language |
| Keyword(2) | deep learning |
| Keyword(3) | Connectionist Temporal Classification |
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| 1st Author's Name | Isogai Hikaru |
| 1st Author's Affiliation | National Institute of Technology, Toyota College (NIT, Toyota College) |
| 2nd Author's Name | Kimura Tsutomu |
| 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) |
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| Speaker | Author-1 |
| Date Time | 2021-03-06 14:40:00 |
| Presentation Time | 25 minutes |
| Registration for | WIT |
| Paper # | WIT2020-38 |
| Volume (vol) | vol.120 |
| Number (no) | no.419 |
| Page | pp.47-52 |
| #Pages | 6 |
| Date of Issue | 2021-02-26 (WIT) |
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