@inproceedings{zhao-etal-2026-continuous,
title = "Continuous Sign Language Recognition using Multimodal Input and Handshape-aware Boundary Detection",
author = "Zhao, Mingyu and
Yang, Zhanfu and
Zhou, Yang and
Xia, Zhaoyang and
Jin, Can and
He, Xiaoxiao and
Lin, Shuhang and
Neidle, Carol and
Metaxas, Dimitri",
editor = "Efthimiou, Eleni and
Fotinea, Stavroula-Evita and
Hanke, Thomas and
Hochgesang, Julie A. and
Mesch, Johanna and
Schulder, Marc",
booktitle = "Proceedings of the {LREC} 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.signlang-1.52/",
doi = "10.63317/22bkv35eirvm",
pages = "501--512",
abstract = "This paper employs a multimodal approach for continuous sign recognition by first using ML for detecting the start and end frames of signs in videos of American Sign Language (ASL) sentences, and then by recognizing the segmented signs. For improved robustness, we use 3D skeletal features extracted from sign language videos to take into account the convergence of sign properties and their dynamics that tend to cluster at sign boundaries. Another focus of this paper is the incorporation of information from 3D hand configuration for boundary detection. To detect handshapes normally expected at the beginning and end of signs, we pretrain a handshape classifier for detection of 87 linguistically defined canonical handshape categories using a dataset that we created by integrating and normalizing several existing datasets. A multimodal fusion module is then used to unify the pretrained sign video segmentation framework and handshape classification models. Finally, the estimated boundaries are used for sign recognition, where the recognition model is trained on a large database containing both citation-form isolated signs and signs pre-segmented (based on manual annotations) from continuous signing{---}as such signs often differ a bit in certain respects. We evaluate our method on the ASLLRP corpus and demonstrate significant improvements over previous work."
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<abstract>This paper employs a multimodal approach for continuous sign recognition by first using ML for detecting the start and end frames of signs in videos of American Sign Language (ASL) sentences, and then by recognizing the segmented signs. For improved robustness, we use 3D skeletal features extracted from sign language videos to take into account the convergence of sign properties and their dynamics that tend to cluster at sign boundaries. Another focus of this paper is the incorporation of information from 3D hand configuration for boundary detection. To detect handshapes normally expected at the beginning and end of signs, we pretrain a handshape classifier for detection of 87 linguistically defined canonical handshape categories using a dataset that we created by integrating and normalizing several existing datasets. A multimodal fusion module is then used to unify the pretrained sign video segmentation framework and handshape classification models. Finally, the estimated boundaries are used for sign recognition, where the recognition model is trained on a large database containing both citation-form isolated signs and signs pre-segmented (based on manual annotations) from continuous signing—as such signs often differ a bit in certain respects. We evaluate our method on the ASLLRP corpus and demonstrate significant improvements over previous work.</abstract>
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%0 Conference Proceedings
%T Continuous Sign Language Recognition using Multimodal Input and Handshape-aware Boundary Detection
%A Zhao, Mingyu
%A Yang, Zhanfu
%A Zhou, Yang
%A Xia, Zhaoyang
%A Jin, Can
%A He, Xiaoxiao
%A Lin, Shuhang
%A Neidle, Carol
%A Metaxas, Dimitri
%Y Efthimiou, Eleni
%Y Fotinea, Stavroula-Evita
%Y Hanke, Thomas
%Y Hochgesang, Julie A.
%Y Mesch, Johanna
%Y Schulder, Marc
%S Proceedings of the LREC 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F zhao-etal-2026-continuous
%X This paper employs a multimodal approach for continuous sign recognition by first using ML for detecting the start and end frames of signs in videos of American Sign Language (ASL) sentences, and then by recognizing the segmented signs. For improved robustness, we use 3D skeletal features extracted from sign language videos to take into account the convergence of sign properties and their dynamics that tend to cluster at sign boundaries. Another focus of this paper is the incorporation of information from 3D hand configuration for boundary detection. To detect handshapes normally expected at the beginning and end of signs, we pretrain a handshape classifier for detection of 87 linguistically defined canonical handshape categories using a dataset that we created by integrating and normalizing several existing datasets. A multimodal fusion module is then used to unify the pretrained sign video segmentation framework and handshape classification models. Finally, the estimated boundaries are used for sign recognition, where the recognition model is trained on a large database containing both citation-form isolated signs and signs pre-segmented (based on manual annotations) from continuous signing—as such signs often differ a bit in certain respects. We evaluate our method on the ASLLRP corpus and demonstrate significant improvements over previous work.
%R 10.63317/22bkv35eirvm
%U https://aclanthology.org/2026.signlang-1.52/
%U https://doi.org/10.63317/22bkv35eirvm
%P 501-512
Markdown (Informal)
[Continuous Sign Language Recognition using Multimodal Input and Handshape-aware Boundary Detection](https://aclanthology.org/2026.signlang-1.52/) (Zhao et al., SignLang 2026)
ACL
- Mingyu Zhao, Zhanfu Yang, Yang Zhou, Zhaoyang Xia, Can Jin, Xiaoxiao He, Shuhang Lin, Carol Neidle, and Dimitri Metaxas. 2026. Continuous Sign Language Recognition using Multimodal Input and Handshape-aware Boundary Detection. In Proceedings of the LREC 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion, pages 501–512, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).