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
Neural Sign Language Translation
TL;DR: This work formalizes SLT in the framework of Neural Machine Translation (NMT) for both end-to-end and pretrained settings (using expert knowledge) and allows to jointly learn the spatial representations, the underlying language model, and the mapping between sign and spoken language.
Book ChapterDOI
Sign Language Recognition Using Convolutional Neural Networks
TL;DR: This contribution considers a recognition system using the Microsoft Kinect, convolutional neural networks (CNNs), and GPU acceleration to recognize 20 Italian gestures with high accuracy, and achieves a mean Jaccard Index of 0.789 in the ChaLearn 2014 Looking at People gesture spotting competition.
Journal ArticleDOI
Continuous Sign Language Recognition: Towards Large Vocabulary Statistical Recognition Systems Handling Multiple Signers
TL;DR: This work presents a statistical recognition approach performing large vocabulary continuous sign language recognition across different signers, and is the first time system design on a large data set with true focus on real-life applicability is thoroughly presented.
Proceedings ArticleDOI
Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison
TL;DR: Wang et al. as mentioned in this paper introduced a large-scale Word-Level American Sign Language (WLASL) video dataset, containing more than 2000 words performed by over 100 signers.
Proceedings ArticleDOI
Recurrent Convolutional Neural Networks for Continuous Sign Language Recognition by Staged Optimization
TL;DR: This work presents a weakly supervised framework with deep neural networks for vision-based continuous sign language recognition, where the ordered gloss labels but no exact temporal locations are available with the video of sign sentence, and the amount of labeled sentences for training is limited.
References
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Book ChapterDOI
Hand tracking and affine shape-appearance handshape sub-units in continuous sign language recognition
TL;DR: The experiments indicate the effectiveness of the overall approach and especially for the modeling of handshapes when incorporated in the HSU-based framework showing promising results.
Proceedings ArticleDOI
Automatic hand trajectory segmentation and phoneme transcription for sign language
W.W. Kong,Surendra Ranganath +1 more
TL;DR: This paper presents an automatic approach to segment 3-D hand trajectories and transcribe phonemes based on them, as a step towards recognizing American sign language (ASL).
Proceedings ArticleDOI
Learning Sequential Patterns for Lipreading
Eng-Jon Ong,Richard Bowden +1 more
TL;DR: A novel machine learning algorithm (SP-Boosting) to tackle the problem of lipreading by building visual sequence classifiers based on visual sequence patterns, which achieves state of the art recognition performane, using only a small set of sequential patterns.
Sign Language Recognition using Linguistically Derived Sub-Units
Helen Cooper,Richard Bowden +1 more
TL;DR: Using a simple Markov model to combine the sub-unit classifiers allows sign level classification giving an average of 63%, over a 164 sign lexicon, with no grammatical constraints.
Proceedings ArticleDOI
Increasing manual sign recognition vocabulary through relabelling
Manjula B. Waldron,Soowon Kim +1 more
TL;DR: The results showed that the overall recognition rate of the relabelled network was 84% as compared to 86% for the retrained network and it was found that the dynamic sampling of the signs made the movement phoneme module unnecessary.