Experimental evaluation demonstrates 99.29 percent accuracy in static Indian sign language recognition, highlighting the potential of vision transformers to bridge communication gaps.
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.
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Kothadiya et al. (2023) studied this question.
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