Abstract:
For many years, researchers are trying to recognize Bengali sign language for helping deafmute people which is very challenging task on the perspective of our country. Every
research has its own margins and is still incapable to be used commercially. For that reason,
de-vice interpreter is obligate to accommodate that deaf and hard-of-hearing community to
communicate with normal people. In this paper, the main target to con-struct a model to
recognize Bengali Character Sign Language using deep leaning approach. For that reason,
we use Convolutional Neural Network (CNN) to train individualsigns. For those individual
signs, we construct a data set called Bengali Ishara-Lipi to achieve our goal. This model is
trained by 5760 preprocessed images and tested by 1440 pictures. The quantitative relation
of the trained and test-ed pictures was 80% and 20% severely. Finally, our model gained
92.7% accuracy to recognize Bengali alphabetical sign language. Our model will avail for
commencing to make Bengali sign language device interpreter.
