The absence of annotated sign language datasets has hindered the development of sign language recognition and translation technologies. In this paper, we introduce Bornil; a crowdsource-friendly, multilingual sign language data collection, annotation, and validation platform. Bornil allows users to record sign language gestures and lets annotators perform sentence and gloss-level annotation. It also allows validators to make sure of the quality of both the recorded videos and the annotations through manual validation to develop high-quality datasets for deep learning-based Automatic Sign Language Recognition. To demonstrate the system's efficacy; we collected the largest sign language dataset for Bangladeshi Sign Language dialect, perform deep learning based Sign Language Recognition modeling, and report the benchmark performance. The Bornil platform, BornilDB v1.0 Dataset, and the codebases are available on https://bornil.bengali.ai
@article{arxiv.2308.15402,
title = {Bornil: An open-source sign language data crowdsourcing platform for AI enabled dialect-agnostic communication},
author = {Shahriar Elahi Dhruvo and Mohammad Akhlaqur Rahman and Manash Kumar Mandal and Md. Istiak Hossain Shihab and A. A. Noman Ansary and Kaneez Fatema Shithi and Sanjida Khanom and Rabeya Akter and Safaeid Hossain Arib and M. N. Ansary and Sazia Mehnaz and Rezwana Sultana and Sejuti Rahman and Sayma Sultana Chowdhury and Sabbir Ahmed Chowdhury and Farig Sadeque and Asif Sushmit},
journal= {arXiv preprint arXiv:2308.15402},
year = {2023}
}