Scopus Indexed Paper

Paper Details

A Potent Model to Recognize Bangla Sign Language Digits Using Convolutional Neural Network
Hearing impaired people have own language called Sign Language but it is difficult for understanding to general people. Sign language is the basic method of communication for deaf people during their everyday of life. Sign digits are also a major part of sign language. So machine translator is necessary to allow them to communicate with general people. For making their language understandable to general people, computer vision based solutions are well known nowadays. In this research work we aims at constructing a model in deep learning approach to recognize Bangla Sign Language (BdSL) digits. In this approach there used Convolutional Neural Network (CNN) to train particular signs with a respective training dataset (Eshara-Lipi) for acquiring our aim. The model trained and tested with respectively 860 training images and 215 (20%) test images of tent classes of digits. Finally, the training model gained about 95% accuracy at recognition of Bangla sign language digits. This model will contribute for moving one step forward to make BdSL machine translator.
BdSL, Bangla Sign Language, CNN, Machine Learning, Deep Learning, NLP, Computer Vision, Pattern Recognition, Sign Digits, Sign Language
Sanzidul Islam(Md.), Sadia Sultana Sharmin Mousumi, AKM Shahariar Azad Rabby, Sayed Akhter Hossain, Sheikh Abujar
Journal or Conference Name
Procedia Computer Science
Publish Year