This project is centered around building a neural network that is able to recognize ASL letters in images, particularly within the scope of a live video feed. Initial testing results came up short of expectations when both the convolutional network and VGG16 transfer learning approaches failed to generalize in settings of different backgrounds. The use of a pre-trained hand joint detection model was then adopted with the produced joint locations being fed into a fully-connected neural network. The results of this approach exceeded those of prior methods and generalized well to a live video feed application.
@article{arxiv.2305.17192,
title = {Live American Sign Language Letter Classification with Convolutional Neural Networks},
author = {Kyle Boone and Ben Wurster and Seth Thao and Yu Hen Hu},
journal= {arXiv preprint arXiv:2305.17192},
year = {2023}
}