Automated Bangla sign language translation system for alphabets by means of MobileNet

Tazkia Mim Angona (Bangladesh University of Professionals)
A. S. M. Siamuzzaman Shaon (Bangladesh University of Professionals)
Kazi Tahmid Rashad Niloy (Bangladesh University of Professionals)
Tajbia Karim (Bangladesh University of Professionals)
Zarin Tasnim (Bangladesh University of Professionals)
S. M. Salim Reza (Bangladesh University of Professionals)
Tasmima Noushiba Mahbub (Bangladesh University of Professionals)

TELKOMNIKA (Telecommunication Computing Electronics and Control)
Vol 18, No 3: June 2020

Individuals with hearing and speaking impairment communicate using sign language. The movement of hand, body and expressions of face are the means by which the people, who are unable to hear and speak, can communicate. Bangla sign alphabets are formed with one or two hand movements. There are some features which differentiates the signs. To detect and recognize the signs, analyzing its shape and comparing its features is necessary. This paper aims to propose a model and build a computer systemthat can recognize Bangla Sign Lanugage alphabets and translate them to corresponding Bangla letters by means of deep convolutional neural network (CNN). CNN has been introduced in this model in form of a pre-trained model called “MobileNet” which produced an average accuracy of 95.71% in recognizing 36 Bangla Sign Language alphabets.