Computer Vision Based Bangla Sign Language Recognition Using Transfer Learning

Human societies have relied on communication since ancient times, yet verbal communication poses significant challenges for deaf and hard-of-hearing individuals, necessitating reliance on sign language. Recent advancements, notably through deep learning models, have propelled research in this area. Acknowledging the need for further progress, particularly in minority languages like Bengali, our study aims to develop a method for image-based Bengali sign language detection. We constructed two independent convolutional neural network (CNN) models, InceptionV3 and Xception, leveraging data from diverse sources. Remarkably, the Inception V3 model achieved an accuracy of 97%, while the Xception model surpassed expectations with an accuracy of 99.50%. These results signify substantial progress, demonstrating the efficacy of deep learning architectures, especially the Xception model, in accurately interpreting Bengali sign language. Our study demonstrated how transfer learning, when combined with careful optimization, may yield remarkable outcomes in Bengali sign language recognition, which are further enhanced by data augmentation methods.