Abstract:
Sign Language is the method of interaction between the hearing-impaired people and the
general people. It is the only way to decrease the communication gap of deaf community
and the normal people. A machine translator could be potent solution for solving this
problem. But collecting hand sign data of sign language from reliable source is too much
difficult to researchers. This project is conceived from the above scenario. In this project,
we made two open access isolated datasets- Ishara-Bochon and Ishara-Lipi and its
recognition model. Ishara-Bochon contains 100 sets of 10 different classes for Bangla Sign
Language digits. And Ishara-Lipi contains 50 sets of 36 classes for Bangla Sign Language
characters. The image data are collected from different deaf and general volunteers from
different institutes. Our datasets could be used to build computer vision based or any other
type of system that allows users to search the meaning of BdSL signs. We attempted to
represent a BdSL recognizer model which will help hearing impaired people to remove
communication gap with generals. In proposed method we used multi-layered
Convolutional Neural Network (CNN). CNNs have capability to learn structures
automatically from raw data. Our model gained 92% accuracy on our digits datasets and
86% accuracy on our characters dataset. In the future, further AI and data analytics will
add values to the services delivered to the end users.
