Project title
DeepSignNet: Video processing for Sign Language Recognition using Deep Bayesian Recurrent Neural Networks
Award URL
https://deepsignnet.cut.ac.cy/
Start date
January 1, 2019
Expected Completion
2021-08-10
Description
Sign Languages (SLs), being the mother tongues of the deaf, are an important part of the European and the world cultural diversity. In Europe, there are 30 official SLs and more than 750000 SL users, while only 12000 interpreters. This shortage undermines the right to equal education and health services, and even endangers the lives of deaf people.
DeepSignNet makes significant contributions to automated visual SL recognition (SLR). We address: (a) Inference of the appropriate machine learning model size to limit the amount of parameters to learn, and (b) integration of prior linguistic constraints and non-manual cues.
To this end, we will collaborate with a world-leading group, the Computational Bioimaging and Modeling (CBIM) Center of Rutgers University – New Jersey. It is one of the leaders in computer vision and has extensive know–how in SL recognition.
DeepSignNet makes significant contributions to automated visual SL recognition (SLR). We address: (a) Inference of the appropriate machine learning model size to limit the amount of parameters to learn, and (b) integration of prior linguistic constraints and non-manual cues.
To this end, we will collaborate with a world-leading group, the Computational Bioimaging and Modeling (CBIM) Center of Rutgers University – New Jersey. It is one of the leaders in computer vision and has extensive know–how in SL recognition.