BTVSL: A Novel Sentence-Level Annotated Dataset for Bangla Sign Language Translation

Sign language, a vital communication tool for individuals with hearing impairments worldwide, is prominently utilized within the BangIa-speaking community. BangIa is recognized as the seventh most spoken language globally. However, research in BangIa Sign Language Recognition (SLR) - the process of translating symbols or words from images and videos - has been predominantly confined to controlled environments with limited samples and rudimentary symbol annotations, impeding its application in real-world scenarios. In contrast to previous studies, our research concentrates on SLR. It delves into the relatively unexplored territories of BangIa Sign Language Translation (SLT) and Sign Language Production (SLP), areas that have been largely overlooked due to dataset constraints. We introduce BTVSL, a comprehensive BangIa Sign Language dataset derived from the YouTube series ‘BTV Desh o Jonopoder Khobor'. This dataset, featuring 60 hours of news content in sign language delivered by professionals, represents the largest sentence-level dataset available for BangIa SLT, encompassing a broad spectrum of expressions. Leveraging BTVSL, we evaluated four distinct SLT models, achieving an average BLEU score of 20.42. This result underscores the potential of BTVSL in enhancing the accuracy of sign language translation.