Static-gesture word recognition in Bangla sign language using convolutional neural network

Kulsum Ara Lipi (Bangladesh University of Professionals)
Sumaita Faria Karim Adrita (Bangladesh University of Professionals)
Zannatul Ferdous Tunny (Bangladesh University of Professionals)
Abir Hasan Munna (Bangladesh University of Professionals)
Ahmedul Kabir (University of Dhaka)

TELKOMNIKA (Telecommunication Computing Electronics and Control)
Vol 20, No 5: October 2022

Sign language is the communication process of people with hearing impairments. For hearing-impaired communication in Bangladesh and parts of India, Bangla sign language (BSL) is the standard. While Bangla is one of the most widely spoken languages in the world, there is a scarcity of research in the field of BSL recognition. The few research works done so far focused on detecting BSL alphabets. To the best of our knowledge, no work on detecting BSL words has been conducted till now for the unavailability of BSL word dataset. In this research, a small static-gesture word dataset has been developed, and a deep learning-based method has been introduced that can detect BSL static-gesture words from images. The dataset, “BSLword” contains 30 static-gesture BSL words with 1200 images for training. The training is done using a multi-layered convolutional neural network with the Adam optimizer. OpenCV is used for image processing and TensorFlow is used to build the deep learning models. This system can recognize BSL static-gesture words with 92.50% accuracy on the word dataset.