Sign Language Recognition
34 papers with code • 6 benchmarks • 14 datasets
Given a signed video input the task is to predict the (sequence of) sign(s) that are performed.
( Image credit: Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison )
Benchmarks
These leaderboards are used to track progress in Sign Language Recognition
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Datasets
WLASL
AUTSL
BosphorusSign22k
GSL
Rendered Handpose Dataset
ASLG-PC12
Most implemented papers
Learning to Estimate 3D Hand Pose from Single RGB Images
lmb-freiburg/hand3d
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ICCV 2017
Low-cost consumer depth cameras and deep learning have enabled reasonable 3D hand pose estimation from single depth images.
Fingerspelling recognition in the wild with iterative visual attention
chevalierNoir/asl-iter-attn
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ICCV 2019
In this paper we focus on recognition of fingerspelling sequences in American Sign Language (ASL) videos collected in the wild, mainly from YouTube and Deaf social media.
Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison
dxli94/WLASL
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Based on this new large-scale dataset, we are able to experiment with several deep learning methods for word-level sign recognition and evaluate their performances in large scale scenarios.
BlazePose: On-device Real-time Body Pose tracking
We present BlazePose, a lightweight convolutional neural network architecture for human pose estimation that is tailored for real-time inference on mobile devices.
TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation
verashira/TSPNet
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NeurIPS 2020
Sign language translation (SLT) aims to interpret sign video sequences into text-based natural language sentences.
Context Matters: Self-Attention for Sign Language Recognition
faresbs/slrt
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For that reason, we apply attention to synchronize and help capture entangled dependencies between the different sign language components.
Skeleton Aware Multi-modal Sign Language Recognition
Sign language is commonly used by deaf or speech impaired people to communicate but requires significant effort to master.
Sign Language Recognition via Skeleton-Aware Multi-Model Ensemble
Current Sign Language Recognition (SLR) methods usually extract features via deep neural networks and suffer overfitting due to limited and noisy data.
Real-time Sign Language Fingerspelling Recognition using Convolutional Neural Networks from Depth map
We train CNNs for the classification of 31 alphabets and numbers using a subset of collected depth data from multiple subjects.
SubUNets: End-To-End Hand Shape and Continuous Sign Language Recognition
We propose a novel deep learning approach to solve simultaneous alignment and recognition problems (referred to as "Sequence-to-sequence" learning).
