Abstract

Various researchers have proposed models with high recognition rates for sign language recognition. Moreover, with the improvement of GPU processing power, more and more proposals for real-time processing have been made. We have proposed methods to improve the recognition rate by adding a new approach to the Skeleton Aware Multi-modal Sign Language Recognition (SAM-SLR) using the Ankara University Turkish Sign Language (AUTSL) dataset. This SAM-SLR uses four modalities and fuses their results to achieve a high recognition rate. However, when processing in real-time, the four modalities require more processing and extended response time than other methods. We propose a method for real-time processing while maintaining the original evaluation value based on the published code of this SAM-SLR. Our proposed method is faster by reducing processing, parallel processing, utilizing internal memory, etc. As a result, the average response time was 0.7248 s for the proposed processing method and 4.4013 s for the non-accelerated serial processing method, an 83.5% improvement with an average processing speed of 6.07 times.