| Autor: |
Deep R. Kothadiya, Chintan M. Bhatt, Tanzila Saba, Amjad Rehman, Saeed Ali Bahaj |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 11, Pp 4730-4739 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2022.3231130 |
| Popis: |
Sign language is the most common form of communication for the hearing impaired. To bridge the communication gap with such impaired people, a normal people should be able to recognize the signs. Therefore, it is necessary to introduce a sign language recognition system to assist such impaired people. This paper proposes the Transformer Encoder as a useful tool for sign language recognition. For the recognition of static Indian signs, the authors have implemented a vision transformer. To recognize static Indian sign language, proposed methodology archives noticeable performance over other state-of-the-art convolution architecture. The suggested methodology divides the sign into a series of positional embedding patches, which are then sent to a transformer block with four self-attention layers and a multilayer perceptron network. Experimental results show satisfactory identification of gestures under various augmentation methods. Moreover, the proposed approach only requires a very small number of training epochs to achieve 99.29 percent accuracy. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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