Book ChapterDOI
Sign language recognition using sub-units
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This paper discusses sign language recognition using linguistic sub-units, presenting three types of sub- units for consideration; those learnt from appearance data as well as those inferred from both 2D or 3D tracking data.Abstract:
This paper discusses sign language recognition using linguistic sub-units. It presents three types of sub-units for consideration; those learnt from appearance data as well as those inferred from both 2D or 3D tracking data. These sub-units are then combined using a sign level classifier; here, two options are presented. The first uses Markov Models to encode the temporal changes between sub-units. The second makes use of Sequential Pattern Boosting to apply discriminative feature selection at the same time as encoding temporal information. This approach is more robust to noise and performs well in signer independent tests, improving results from the 54% achieved by the Markov Chains to 76%.read more
Citations
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Proceedings ArticleDOI
A new system for Chinese sign language recognition
TL;DR: This paper proposes a new system for isolated sign language recognition (SLR) and continuous SLR, and proposes a Dynamic Programming method with warping templates obtained by Dynamic Time Warping (DTW) algorithm.
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Sign Language Translation with Transformers.
TL;DR: It is demonstrated that end-to-end translation on predicted glosses provides even better performance than translation on ground truth glosses, and shows potential for further improvement in SLT by either jointly training the SLR and translation systems or by revising the gloss annotation system.
Proceedings ArticleDOI
Pose-based Sign Language Recognition using GCN and BERT
TL;DR: In this article, the spatial and temporal dependencies between the frames are captured using Bidirectional Encoder Representations from Transformers (BERT) and Graph Convolutional Network (GCN) respectively.
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Detection of major ASL sign types in continuous signing for ASL recognition
TL;DR: This paper presents a multiple instance learning-based segmentation system that accurately labels 91.27% of the video frames of 500 continuous utterances from the publicly accessible NCSLGR corpus (Neidle and Vogler, 2012).
Journal ArticleDOI
Automatic Technologies for Processing Spoken Sign Languages
TL;DR: This paper presents a computer system for text-to-sign language synthesis for the Russian and Czech Sign Languages and suggests that sign languages may be considered as non-written under-resourced spoken languages.
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