Publication

Abstract:
The present paper describes an ongoing project on designing and creating the First Qatari Sign Language dataset called “Jumla Dataset: The Jumla Qatari Sign Language Corpus” with intra-linguistic and extra-linguistic levels of the written Arabic text. The annotation of videos in Qatari Sign Language (QSL) takes input from signers to identify the Arabic glosses components toward representing the QSL in a written way with high accuracy, furthermore to the use of the annotation output in the development of computational Sign Language tools. The QSL annotation is based on an input of 4 videos recorded by deaf persons or Sign Language interpreters from different angles (front, left side, right side, and facial view). The output is a JSON file containing all the interpreted sentences given as an entry record. The glosses are annotated for each period and aligned with the Arabic content. Moreover, the presented tool, available as an open-source, provides a management system to classify all records from cameras, motion capture systems, and edited files in addition to the possibility to create components for each gloss annotation terminology depending on the target Sign Language.