Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning

By S. W. Ong, Surendra Ranganath. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005

639 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Developmental and Educational Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/852e333b06fbe9ea5bf3dd1e894cad86283b94f7

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Citations in the corpus

American sign language recognition with the kinect (2011, background): “Ong lists the following categories in his survey paper [15]: two dimensional segmentation, two dimensional moment-based, motion vectors, three dimensional hand positions and three dimensional hand orientations.”
A Survey of Glove-Based Systems and Their Applications (2008, methodology): “The reader is referred to [89], [90] for a detailed description of methods for data clas-”
A Survey on Human Motion Analysis from Depth Data (2013, background): “Recognizing signs of visual-gestural languages like the American sign language (ASL) is a very active field [145, 146, 147, 148, 149, 150, 151].”
A Deep Neural Framework for Continuous Sign Language Recognition by Iterative Training (2019, background): “Different from these problems, continuous SL recognition is to transcribe videos of SL sentences to ordered sequences of glosses (here we use “gloss” to represent a gesture with its closest meaning in natural languages [1]), and the continuous video streams are provided without prior segmentation.”
Recent methods and databases in vision-based hand gesture recognition: A review (2015, background): “An analsis of sign languages, grammatical processes in sign gestures, and ssues relevant to the automatic recognition of sign languages are disussed in [9].”
Spelling it out: Real-time ASL fingerspelling recognition (2011, background): “Sign language recognition is a very hard problem, and despite recent progresses automatic sign language recognition systems are still in their infancy (see, eg, [11, 9]).”
Sign Language Recognition (2011, background): “From the review of SLR by Ong and Ranganath [82], one of their main observations is the lack of attention that non-manual features has received in the literature.”
Improving Sign Language Translation with Monolingual Data by Sign Back-Translation (2021, neutral): “Early works in isolated SLR utilize hand-crafted features [31, 41] for sign description.”
Continuous sign language recognition: Towards large vocabulary statistical recognition systems handling multiple signers (2015, background): “[52] Ong, S.”
Recurrent Convolutional Neural Networks for Continuous Sign Language Recognition by Staged Optimization (2017, background): “(We usually use “gloss” to represent sign with its closest meaning in natural languages [24].”