Classification of American Sign Language by Applying a Transfer Learned Deep Convolutional Neural Network

Having the community with more than 500,000 deaf and mute English speaking people, sign alphabet detection has become a domain of interest among the researchers for the last decade. Previously, a lot of signs of progress have been made in accurate recognition of the American Sign Language. Both convolutional neural networks and traditional machine learning classifiers have been applied formerly for the recognition process. In this study, we've considered an American Sign Language dataset having 36 classes of English characters and digits. The latest research on this real-world dataset achieved an accuracy of 90%. However, in our research, we introduced modified inceptionV3 architecture for the detection of American Sign characters and obtained overall correctness of 98.81% which outperformed all the previous studies by a notable margin.