Abstract
Isolated Sign Language Recognition (ISLR), which seeks to automatically align sign videos with corresponding glosses, has recently gained considerable attention from the artificial intelligence community. This technology has the potential to bridge the communication gap between hearing people and the deaf community. However, the development of ISLR is hindered by the scarcity of sign language datasets. Moreover, existing ISLR datasets are limited by their provision of a single perspective, which makes hand gesture occlusion difficult to handle. In addition, existing Chinese ISLR datasets, such as DEVISIGN and NMFs-CSL, fail to cover the entire vocabulary of Chinese National Sign Language (CNSL). This greatly obstructs the application of ISLR in the real world. To address these challenges, we introduce a novel word-level sign language dataset for ISLR that encompasses the entire CNSL vocabulary, comprising 6,707 unique signs. Moreover, it provides two perspectives of signers: the front side and the left side. There are ten signers involved in sign video recording, and the processes of sign video recording, annotation and quality assurance were rigorously controlled. To the best of our knowledge, this dataset is the first dual-view Chinese sign language dataset for ISLR that covers all the sign words in CNSL.