AzSLD: Azerbaijani Sign Language Dataset for Fingerspelling, Word, and Sentence Translation with Baseline Software
Nigar Alishzade, Jamaladdin Hasanov
TL;DR
The paper addresses the scarcity of Azerbaijani Sign Language resources for recognition and translation tasks. It introduces AzSLD, a multi-component, open-source dataset comprising Fingerspelling, Words, and Sentences, totaling about 30,312 videos (~65 hours) captured from diverse signers with two camera angles and frame-aligned Azerbaijani translations. The dataset is released under CC BY 4.0, with comprehensive documentation and a publicly available data loader to facilitate training and evaluation. By providing both isolated and continuous signing data, along with ethical guidelines and community involvement, AzSLD aims to advance SLR and SLT research for the Azerbaijani Deaf community.
Abstract
Sign language processing technology development relies on extensive and reliable datasets, instructions, and ethical guidelines. We present a comprehensive Azerbaijani Sign Language Dataset (AzSLD) collected from diverse sign language users and linguistic parameters to facilitate advancements in sign recognition and translation systems and support the local sign language community. The dataset was created within the framework of a vision-based AzSL translation project. This study introduces the dataset as a summary of the fingerspelling alphabet and sentence- and word-level sign language datasets. The dataset was collected from signers of different ages, genders, and signing styles, with videos recorded from two camera angles to capture each sign in full detail. This approach ensures robust training and evaluation of gesture recognition models. AzSLD contains 30,000 videos, each carefully annotated with accurate sign labels and corresponding linguistic translations. The dataset is accompanied by technical documentation and source code to facilitate its use in training and testing. This dataset offers a valuable resource of labeled data for researchers and developers working on sign language recognition, translation, or synthesis. Ethical guidelines were strictly followed throughout the project, with all participants providing informed consent for collecting, publishing, and using the data.