Given a video containing sign language, the task is to predict the translation into (written) spoken language.
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Sign language translation (SLT) aims to interpret sign video sequences into text-based natural language sentences.
Based on this new large-scale dataset, we are able to experiment with several deep learning methods for word-level sign recognition and evaluate their performances in large scale scenarios.
SLR seeks to recognize a sequence of continuous signs but neglects the underlying rich grammatical and linguistic structures of sign language that differ from spoken language.
This contradicts previous claims that GT gloss translation acts as an upper bound for SLT performance and reveals that glosses are an inefficient representation of sign language.
Ranked #1 on Sign Language Translation on ASLG-PC12