SignGPT and the Visual Language Toolkit
Published:
| SignGPT and the Visual Language Toolkit was accepted at the 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion, LREC 2026, Palma de Mallorca, Spain. Preprint: Available here (PDF) Venue: 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion, LREC 2026 Authors: Brown, M., Ranum, O., Fish, E., Proctor, H., Woll, B., Bowden, R., Cormier, K. |
Abstract: SignGPT’s Visual Language Toolkit (VLT) aims to remove fundamental barriers to large scale sign language modelling by developing data-driven, linguistically grounded methods for continuous sign language recognition. We first identify fundamental issues around the ecological validity of potential data sources (e.g. broadcast media with interpreted signing or captions, scraping of social media). We contrast these with the currently highly resource-intensive development of curated sign language corpora based on linguistic principles. The VLT addresses this scarcity of high quality sign language data by providing semi-automated glossing and other recognition tools, driving large scale corpus expansion without sacrificing linguistic principles. Unlike prior systems that rely on sparse glossing, the project integrates dense temporal annotation, non-manual and non-lexical feature tracking, and transformer-based architectures to capture the multimodal and spatial structure of signing. By aligning machine vision innovation with linguistic insights and community-embedded evaluation, SignGPT establishes a foundation for robust and extensible sign language models.