  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.
