Real-Time Sign Language Detection Using CNN
Md. Nafis Saiful, Abdulla Al Isam, Hamim Ahmed Moon, Rifa Tammana Jaman, Mitul Das, Md. Raisul Alam, Ashifur Rahman
Abstract
Sign language is a system of communication using visual gestures and signs. Hearing impaired people and the deaf and dumb community use sign language as their only means of communication. Understanding sign language is so much difficult for a normal person. Therefore, the minority group has always faced many difficulties in communicating with the general population. In this research paper, we proposed a new deep learning-based approach to detect sign language, which can remove the barrier of communication between normal and deaf people. To detect real-time sign language first we prepared a dataset that contains 11 sign words. We used these sign words to train our customized CNN model. We did some preprocessing in the dataset before the training of the CNN model. In our findings, we see that the customized CNN model can achieve the highest 98.6% accuracy, 99% precision, 99% recall and 99% f1-score on the test dataset.