| dc.description.abstract | Pose Estimation features have been successfully used in human activity recognition including sign language
recognition. One of the key challenges in sign language recognition is handling signer-independent modes
and hand dominance of signer. This paper proposes the use of the Gated Recurrent Unit (GRU) with the
ELU activation function to improve computation efficiency and to enhance model learning efficiency. In
addition, the paper proposes two stream model architecture to address the challenge of left and right-hand
dominance. The study developed model using a Tanzania Sign language datasets collected using mobile devices
and extracted pose estimation feature using MediaPipe holistic framework. According to the results, the
proposed model not only achieves an impressive overall accuracy of 95%, but also trains more efficiently
than comparable algorithms. Particularly in the signer-independent mode, the two-stream approach led to
substantial improvements, achieving a maximum accuracy of 92% and a minimum accuracy of 70% with
significant increase on the left handed signer accuracy by 37%. The results highlight the effectiveness of the
two-stream approach in overcoming challenges related to left and right-hand dominance, which often arise
from signer-specific hand dominance. Additionally, the results indicate that, the proposed model can have a
positive impact on limited computational resources while also enhancing the model’s overall performance. | en_US |
