Sign Language Recognition
67 papers with code • 10 benchmarks • 19 datasets
Sign Language Recognition is a computer vision and natural language processing task that involves automatically recognizing and translating sign language gestures into written or spoken language. The goal of sign language recognition is to develop algorithms that can understand and interpret sign language, enabling people who use sign language as their primary mode of communication to communicate more easily with non-signers.
( 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
How2Sign
AUTSL
BosphorusSign22k
GSL
Content4All
Most implemented papers
Visual Alignment Constraint for Continuous Sign Language Recognition
ycmin95/VAC_CSLR
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ICCV 2021
Specifically, the proposed VAC comprises two auxiliary losses: one focuses on visual features only, and the other enforces prediction alignment between the feature extractor and the alignment module.
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.
Self-Emphasizing Network for Continuous Sign Language Recognition
To relieve this problem, we propose a self-emphasizing network (SEN) to emphasize informative spatial regions in a self-motivated way, with few extra computations and without additional expensive supervision.
Improving Sign Recognition with Phonology
leekezar/improvingsignrecognitionwithphonology
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We use insights from research on American Sign Language (ASL) phonology to train models for isolated sign language recognition (ISLR), a step towards automatic sign language understanding.
Improving Continuous Sign Language Recognition with Adapted Image Models
Besides, fully fine-tuning the model easily forgets the generic essential knowledge acquired in the pretraining stage and overfits the downstream 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.
A Study of Convolutional Architectures for Handshape Recognition applied to Sign Language
midusi/convolutional_handshape
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CACIC 2017
Using the LSA16 and RWTH-PHOENIX-Weather handshape datasets, we performed experiments with the LeNet, VGG16, ResNet-34 and All Convolutional architectures, as well as Inception with normal training and via transfer learning, and compared them to the state of the art in these datasets.
Neural Sign Language Translation
neccam/nslt
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CVPR 2018
SLR seeks to recognize a sequence of continuous signs but neglects the underlying rich grammatical and linguistic structures of sign language that differ from spoken language.
Temporal Unet: Sample Level Human Action Recognition using WiFi
geekfeiw/WiSLAR
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In this task, every WiFi distortion sample in the whole series should be categorized into one action, which is a critical technique in precise action localization, continuous action segmentation, and real-time action recognition.
A Deep Neural Framework for Continuous Sign Language Recognition by Iterative Training
iliasprc/slrzoo
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IEEE Transactions on Multimedia 2019
In contrast, our proposed architecture adopts deep convolutional neural networks with stacked temporal fusion layers as the feature extraction module, and bi-directional recurrent neural networks as the sequence learning module.
