Improving Sign Language Understanding Introducing Label Smoothing

Sihan Tan, Khan Nabeela Khanum, Katsutoshi Itoyama, Kazuhiro Nakadai

Abstract: Sign language is one of the most important communication methods when considering equality, diversity, and inclusion. Sign language understanding implies understanding sign language using machines, and it involves mainly two functions; sign language recognition and sign language translation. To improve sign language understanding performance, this paper proposes to use label smoothing with CTC (Connectionist Temporal Classification) loss as training criteria for the sign language understanding neural network. Experimental results showed the effectiveness of the proposed method in both sign language recognition and translation.

External IDs:dblp:conf/ro-man/SihanKIN23