Two stream GRU model with ELU activation function for sign language recognition

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. • Propose GRU with ELU activation function for pose estimation features. • Propose two stream architecture for hand dominance challenge. • Evaluate model performance on signer independent. • Evaluate model performance on signer hand dominance.