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Sign Language Recognition Using Graph and General Deep Neural Network Based on Large Scale Dataset
IEEE AccessSign Language Recognition (SLR) represents a revolutionary technology aiming to establish communication between hearing impaired and non-hearing impaired communities, surpassing traditional interpreter-based approaches. Existing efforts in automatic sign
Abu Saleh Musa Miah +2 more
exaly +3 more sources
Boundary-Adaptive Encoder With Attention Method for Chinese Sign Language Recognition
IEEE Access, 2021 The sign language signal has hierarchically related information over short and long distances. Due to the intricate temporal correlation of input sequences, Chinese sign language recognition (SLR) has a modeling challenge. The conventional encoders based
Shiliang Huang, Zhongfu Ye
doaj +1 more source
Computer Methods and Programs in Biomedicine Update, 2022 Background: Sign language is an essential means of communication for hearing-impaired individuals. Objective: We aimed to develop an American sign language recognition dataset and use it in the deep learning model which depends on neural networks to ...
Ahmed KASAPBAŞI +3 more
doaj +1 more source
Multi-Information Spatial–Temporal LSTM Fusion Continuous Sign Language Neural Machine Translation
IEEE Access, 2020 There are two basic problems in sign language recognition (SLR): (a) isolated word SLR and (b) continuous SLR. Most of the existing continuous SLR methods are extensions of the isolated word SLR methods. These methods use the isolated word SLR results as
Qinkun Xiao +3 more
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Sign Language Recognition [PDF]
, 2011 This chapter covers the key aspects of sign-language recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a précis of sign linguistics and their impact on the field.
Brian Holt +5 more
core +3 more sources
Sensors, 2021 Sign language is the most important way of communication for hearing-impaired people. Research on sign language recognition can help normal people understand sign language. We reviewed the classic methods of sign language recognition, and the recognition
Lu Meng, Ronghui Li
doaj +1 more source
Exploring MediaPipe optimization strategies for real-time sign language recognition
CTU Journal of Innovation and Sustainable Development, 2023 The present study meticulously investigates optimization strategies for real-time sign language recognition (SLR) employing the MediaPipe framework. We introduce an innovative multi-modal methodology, amalgamating four distinct Long Short-Term Memory ...
Phuoc Thanh Nguyen +5 more
doaj +3 more sources
Chinese sign language recognition based on surface electromyography and motion information.
PLoS ONE, 2023 Sign language (SL) has strong structural features. Various gestures and the complex trajectories of hand movements bring challenges to sign language recognition (SLR).
Wenyu Li +3 more
doaj +1 more source
Hardware acceleration of number theoretic transform for zk‐SNARK
Engineering Reports, EarlyView., 2023 An FPGA‐based hardware accelerator with a multi‐level pipeline is designed to support the large‐bitwidth and large‐scale NTT tasks in zk‐SNARK. It can be flexibly scaled to different scales of FPGAs and has been equipped in the heterogeneous acceleration system with the help of HLS and OpenCL.
Haixu Zhao +6 more
wiley +1 more source
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 Sign language recognition (SLR) is a weakly supervised task that annotates sign videos as textual glosses. Recent studies show that insufficient training caused by the lack of large-scale available sign datasets becomes the main bottleneck for SLR. Most SLR works thereby adopt pretrained visual modules and develop two mainstream solutions.
Jiangbin Zheng 0002 +7 more
openaire +2 more sources