The In-Car Sign Language Corpus (ICSL): A Multi-Modal Resource for Constrained-Space Sign Language Recognition
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Computer Science > Computation and Language
arXiv:2607.11341 (cs)
[Submitted on 13 Jul 2026]
Title:The In-Car Sign Language Corpus (ICSL): A Multi-Modal Resource for Constrained-Space Sign Language Recognition
Authors:Raviteja Boddu, Guilherme Vieira Leite, Joed Lopes da Silva, Ângelo Benetti, Isabela Barbieri, Natália de Melo Afonso, Thyago Santos, Helio Pedrini, Felipe Venâncio Barbosa, José Mario De Martino, Munir Georges, Alessandro Zimmer
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Abstract:This paper addresses the challenges of using sign language within shared mobility services, such as taxis, carpools, or ride-sharing platforms. The use of sign language recognition (SLR) in real-world, confined environments, specifically vehicle interiors remains largely unexplored. To motivate research in this area, we present the In-Car Sign Language (ICSL) dataset for Brazilian Sign Language (Libras), with the long-term goal of improving public transport accessibility for the Deaf and Hard-of-Hearing community. The dataset consists of: (1) high-precision laboratory motion capture (MoCap) data to establish an idealized linguistic baseline and (2) real-world multi-modal in-car recordings captured using a 2D camera and 3D Time-of-Flight sensors. The dataset provides a basis for comparative analyses between synthesized signing avatar animations and recorded real signing interpreter videos, which enable future research into robust "in-the-wild" SLR models and domain adaptation. We describe in detail the use cases, the setup, the data collection protocol, and the metadata structure of the corpus. In total, we recorded a multimodal dataset exceeding 1.5 million frames, comprising the synchronized multimodal streams described above featuring Libras users across various in-car scenarios. The corpus is provided with gloss annotation of lexical signs and non-lexical sign language elements specially designed to support the training and evaluation of deep neural networks for constrained space recognition. In-vehicle signing offers a technically significant example of a constrained, occluded, and non-frontal environment. While recognizing the diverse communication strategies already employed by the Deaf community, identifying automotive-specific limitations provides a useful stepping stone for research into enhancing in-car accessibility and passenger quality of life.
| Comments: | Published in the Proceedings of the LREC2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion Original publication: this https URL The paper is distributed under the CC BY-NC 4.0 license. Link to paper: this https URL |
| Subjects: | Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2607.11341 [cs.CL] |
| (or arXiv:2607.11341v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.11341
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | Proceedings of the LREC2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion |
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View a PDF of the paper titled The In-Car Sign Language Corpus (ICSL): A Multi-Modal Resource for Constrained-Space Sign Language Recognition, by Raviteja Boddu and 11 other authors
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