SOTAVerified

Sign Language Recognition

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 )

Papers

Showing 201–250 of 297 papers

TitleStatusHype
RGB2Hands: Real-Time Tracking of 3D Hand Interactions from Monocular RGB Video—0
Improving Sign Language Translation with Monolingual Data by Sign Back-Translation—0
ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research—0
Evaluating the Immediate Applicability of Pose Estimation for Sign Language Recognition—0
Continuous Sign Language Recognition through a Context-Aware Generative Adversarial Network—0
Read and Attend: Temporal Localisation in Sign Language Videos—0
Sign Language Production: A ReviewCode0
Efficient sign language recognition system and dataset creation method based on deep learning and image processing—0
Application of Transfer Learning to Sign Language Recognition using an Inflated 3D Deep Convolutional Neural NetworkCode0
KArSL: Arabic Sign Language DatabaseCode0
Towards Performance Improvement in Indian Sign Language Recognition—0
Pose-based Sign Language Recognition using GCN and BERT—0
Independent Sign Language Recognition with 3D Body, Hands, and Face Reconstruction—0
A Dataset for Linguistic Understanding, Visual Evaluation, and Recognition of Sign Languages: The K-RSL—0
Position and Rotation Invariant Sign Language Recognition from 3D Kinect Data with Recurrent Neural NetworksCode0
American Sign Language Identification Using Hand Trackpoint Analysis—0
Boosting Continuous Sign Language Recognition via Cross Modality Augmentation—0
Score-level Multi Cue Fusion for Sign Language Recognition—0
Visual Methods for Sign Language Recognition: A Modality-Based Review—0
American Sign Language Recognition Using RF Sensing—0
Global-local Enhancement Network for NMFs-aware Sign Language Recognition—0
AUTSL: A Large Scale Multi-modal Turkish Sign Language Dataset and Baseline Methods—0
Fully Convolutional Networks for Continuous Sign Language Recognition—0
A Comprehensive Study on Deep Learning-based Methods for Sign Language RecognitionCode0
Two-stream Fusion Model for Dynamic Hand Gesture Recognition using 3D-CNN and 2D-CNN Optical Flow guided Motion Template—0
Exploiting the Logits: Joint Sign Language Recognition and Spell-Correction—0
Continuous Sign Language Recognition Through Cross-Modal Alignment of Video and Text Embeddings in a Joint-Latent Space—0
Unsupervised Term Discovery for Continuous Sign Language—0
Sign Language Recognition with Transformer Networks—0
TheRuSLan: Database of Russian Sign Language—0
LSE\_UVIGO: A Multi-source Database for Spanish Sign Language Recognition—0
Improving and Extending Continuous Sign Language Recognition: Taking Iconicity and Spatial Language into account—0
Evaluation of Manual and Non-manual Components for Sign Language Recognition—0
Continuous sign language recognition from wearable IMUs using deep capsule networks and game theory—0
Convolutional Neural Network Array for Sign Language Recognition using Wearable IMUs—0
BosphorusSign22k Sign Language Recognition Dataset—0
Temporal Accumulative Features for Sign Language Recognition—0
Generative Multi-Stream Architecture For American Sign Language Recognition—0
Transferring Cross-domain Knowledge for Video Sign Language Recognition—0
FineHand: Learning Hand Shapes for American Sign Language Recognition—0
Facial Expression Phoenix (FePh): An Annotated Sequenced Dataset for Facial and Emotion-Specified Expressions in Sign Language—0
Spatial-Temporal Multi-Cue Network for Continuous Sign Language Recognition—0
Lightweight and Unobtrusive Data Obfuscation at IoT Edge for Remote InferenceCode0
Trajectory-Based Recognition of Dynamic Persian Sign Language Using Hidden Markov Model—0
SignCol: Open-Source Software for Collecting Sign Language GesturesCode0
Sign Language Recognition Analysis using Multimodal Data—0
SF-Net: Structured Feature Network for Continuous Sign Language Recognition—0
Improving American Sign Language Recognition with Synthetic Data—0
Zero-Shot Sign Language Recognition: Can Textual Data Uncover Sign Languages?—0
A Deep Neural Framework for Continuous Sign Language Recognition by Iterative TrainingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SubUNetsWord Error Rate (WER)40.7—Unverified
2CTF-MMWord Error Rate (WER)37.8—Unverified
3DTNWord Error Rate (WER)36.5—Unverified
4SANWord Error Rate (WER)29.7—Unverified
5Stochastic CSLRWord Error Rate (WER)25.3—Unverified
6CrossModalWord Error Rate (WER)24—Unverified
7SLRGANWord Error Rate (WER)23.4—Unverified
8DNFWord Error Rate (WER)22.86—Unverified
9VACWord Error Rate (WER)22.1—Unverified
10MSKA-SLRWord Error Rate (WER)22.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Stochastic CSLRWord Error Rate (WER)26.1—Unverified
2CrossModalWord Error Rate (WER)24.3—Unverified
3SignBTWord Error Rate (WER)23.9—Unverified
4MMTLBWord Error Rate (WER)22.45—Unverified
5SMKDWord Error Rate (WER)22.4—Unverified
6STMCWord Error Rate (WER)21—Unverified
7WRNN + LETWord Error Rate (WER)20.73—Unverified
8MSKA-SLRWord Error Rate (WER)20.5—Unverified
9C2SLRWord Error Rate (WER)20.4—Unverified
10SignBERT+Word Error Rate (WER)19.9—Unverified
#ModelMetricClaimedVerifiedStatus
1BN-TIN+Transf.Word Error Rate (WER)33.1—Unverified
2C2SLRWord Error Rate (WER)31—Unverified
3SENWord Error Rate (WER)30.7—Unverified
4AdaBrowseWord Error Rate (WER)30.6—Unverified
5CorrNetWord Error Rate (WER)30.1—Unverified
6CTCAWord Error Rate (WER)29.4—Unverified
7TCNetWord Error Rate (WER)29.3—Unverified
8CorrNet+ACDRWord Error Rate (WER)29—Unverified
9MSKA-SLRWord Error Rate (WER)27.8—Unverified
10Swin-MSTPWord Error Rate (WER)27.1—Unverified
#ModelMetricClaimedVerifiedStatus
1STF+LSTMRank-1 Recognition Rate0.99—Unverified
2SAM-SLR (RGB-D)Rank-1 Recognition Rate0.99—Unverified
33D-DCNN + ST-MGCNRank-1 Recognition Rate0.98—Unverified
4Ensemble - NTISRank-1 Recognition Rate0.96—Unverified
5HWGATRank-1 Recognition Rate0.96—Unverified
6MViT-SLRRank-1 Recognition Rate0.96—Unverified
7FE+LSTMRank-1 Recognition Rate0.93—Unverified
8VTN-PFRank-1 Recognition Rate0.93—Unverified
9CNN+FPM+BLSTM+Attention (RGB-D)Rank-1 Recognition Rate0.62—Unverified
#ModelMetricClaimedVerifiedStatus
1Logos-PretrainingTop-1 Accuracy66.82—Unverified
2Uni-SignTop-1 Accuracy63.52—Unverified
3NLA-SLRTop-1 Accuracy61.26—Unverified
4StepNetTop-1 Accuracy61.17—Unverified
5SAM-SLRTop-1 Accuracy58.73—Unverified
6SWIN-SLRTop-1 Accuracy58.51—Unverified
7HWGATTop-1 Accuracy48.49—Unverified
8I3D (pretraining: BSL-1K)Top-1 Accuracy46.82—Unverified
9I3DTop-1 Accuracy32.48—Unverified
#ModelMetricClaimedVerifiedStatus
1Uni-SignTop-1 Accuracy92.25—Unverified
2SiformerTop-1 Accuracy86.5—Unverified
3SignBERTTop-1 Accuracy83.3—Unverified
4I3D, ST-GCNTop-1 Accuracy81.38—Unverified
5StepNetTop-1 Accuracy78.29—Unverified
6I3DTop-1 Accuracy65.89—Unverified
7SPOTERTop-1 Accuracy63.18—Unverified
#ModelMetricClaimedVerifiedStatus
1HandReader_RGBCER (%)30.7—Unverified
2HandReader_KPCER (%)28—Unverified
3HandReader_RGBCER (%)27.6—Unverified
4HandReader_RGB_KPCER (%)27.1—Unverified
5HandReader_KPCER (%)26.2—Unverified
6HandReader_RGB+KPCER (%)24.4—Unverified
#ModelMetricClaimedVerifiedStatus
1SPOTERAccuracy (%)100—Unverified
2SiformerAccuracy (%)99.84—Unverified
3HWGATAccuracy (%)98.59—Unverified
4Bag of words fusion of hand pose/movement/positionAccuracy (%)97—Unverified
53DGCNAccuracy (%)94.84—Unverified
#ModelMetricClaimedVerifiedStatus
1HandReader_RGBCER (%)7.61—Unverified
2HandReader_KPCER (%)7.35—Unverified
3HandReader_RGB_KPCER (%)5.06—Unverified
#ModelMetricClaimedVerifiedStatus
1Uni-SignP-I Top-1 Accuracy78.16—Unverified
2SignBERT+P-I Top-1 Accuracy73.71—Unverified
#ModelMetricClaimedVerifiedStatus
1StepNetTop-1 Accuracy61.17—Unverified
2SignBERT+Top-1 Accuracy55.59—Unverified
#ModelMetricClaimedVerifiedStatus
1StepNetActions Top-177.1—Unverified
#ModelMetricClaimedVerifiedStatus
1MobileNetV2_TSMAccuracy (Top-1)83.6—Unverified
#ModelMetricClaimedVerifiedStatus
1HWGATTop-1 Accuracy93.86—Unverified
#ModelMetricClaimedVerifiedStatus
13D-DCNN + ST-MGCNRank-1 Recognition Rate0.98—Unverified
#ModelMetricClaimedVerifiedStatus
1Skeleton Image RepresentationAccuracy82—Unverified
#ModelMetricClaimedVerifiedStatus
1Skeleton Image RepresentationAccuracy93—Unverified
#ModelMetricClaimedVerifiedStatus
1mVITv2-SMean Accuracy64.09—Unverified