Learning Sign Language Representation using CNN LSTM, 3DCNN, CNN RNN LSTM and CCN TD
This work addresses the need for automated sign language learning tools for new users, but it is incremental as it compares existing methods on new data.
The paper tackled the problem of real-time sign language translation and accuracy grading by evaluating neural network algorithms on Trinidad and Tobago Sign Language and American Sign Language datasets, finding that 3DCNN achieved 91% accuracy on TTSL and 83% on ASL.
Existing Sign Language Learning applications focus on the demonstration of the sign in the hope that the student will copy a sign correctly. In these cases, only a teacher can confirm that the sign was completed correctly, by reviewing a video captured manually. Sign Language Translation is a widely explored field in visual recognition. This paper seeks to explore the algorithms that will allow for real-time, video sign translation, and grading of sign language accuracy for new sign language users. This required algorithms capable of recognizing and processing spatial and temporal features. The aim of this paper is to evaluate and identify the best neural network algorithm that can facilitate a sign language tuition system of this nature. Modern popular algorithms including CNN and 3DCNN are compared on a dataset not yet explored, Trinidad and Tobago Sign Language as well as an American Sign Language dataset. The 3DCNN algorithm was found to be the best performing neural network algorithm from these systems with 91% accuracy in the TTSL dataset and 83% accuracy in the ASL dataset.