SL-ReDu
| Autor: | Gerasimos Potamianos, Stavroula-Evita Fotinea, Petros Maragos, Katerina Papadimitriou, Galini Sapountzaki, Eleni Efthimiou |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Exploit business.industry Computer science Deep learning Feature extraction Greek Sign Language Sign (semiotics) Timeline 02 engineering and technology computer.software_genre Convolutional neural network language.human_language 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering language Eye tracking 020201 artificial intelligence & image processing Artificial intelligence business computer Natural language processing |
| Zdroj: | PETRA |
| DOI: | 10.1145/3389189.3398006 |
| Popis: | We present SL-ReDu, a recently commenced innovative project that aims to exploit deep-learning progress to advance the state-of-the-art in video-based automatic recognition of Greek Sign Language (GSL), while focusing on the use-case of GSL education as a second language. We first briefly overview the project goals, focal areas, and timeline. We then present our initial deep learning-based approach for GSL recognition that employs efficient visual tracking of the signer hands, convolutional neural networks for feature extraction, and attention-based encoder-decoder sequence modeling for sign prediction. Finally, we report experimental results for small-vocabulary, isolated GSL recognition on the single-signer "Polytropon" corpus. To our knowledge, this work constitutes the first application of deep-learning techniques to GSL. |
| Databáze: | OpenAIRE |
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