Real Time Bangladeshi Sign Language Detection using Faster R-CNN
This addresses the problem of real-time communication for hearing-impaired people in Bangladesh, though it is incremental as it applies an existing object detection method to a new dataset.
The paper tackles real-time detection of Bangladeshi Sign Language (BdSL) from images using a Faster R-CNN approach, achieving successful identification and recognition in real time as demonstrated by experimental results.
Bangladeshi Sign Language (BdSL) is a commonly used medium of communication for the hearing-impaired people in Bangladesh. Developing a real time system to detect these signs from images is a great challenge. In this paper, we present a technique to detect BdSL from images that performs in real time. Our method uses Convolutional Neural Network based object detection technique to detect the presence of signs in the image region and to recognize its class. For this purpose, we adopted Faster Region-based Convolutional Network approach and developed a dataset $-$ BdSLImset $-$ to train our system. Previous research works in detecting BdSL generally depend on external devices while most of the other vision-based techniques do not perform efficiently in real time. Our approach, however, is free from such limitations and the experimental results demonstrate that the proposed method successfully identifies and recognizes Bangladeshi signs in real time.