Sign Language Recognition On Rwth Phoenix

Metrics

Word Error Rate (WER)

Results

Performance results of various models on this benchmark

Model Name	Word Error Rate (WER)	Paper Title	Repository
SLRGAN	23.4	007: Democratically Finding The Cause of Packet Drops	-
WRNN + LET	20.89	Multimodal Locally Enhanced Transformer for Continuous Sign Language Recognition	-
SubUNets	40.7	SubUNets: End-To-End Hand Shape and Continuous Sign Language Recognition	-
SAN	29.7	Context Matters: Self-Attention for Sign Language Recognition
VAC	22.1	Visual Alignment Constraint for Continuous Sign Language Recognition
SignBERT+	20	SignBERT+: Hand-model-aware Self-supervised Pre-training for Sign Language Understanding	-
DNF	22.86	A Deep Neural Framework for Continuous Sign Language Recognition by Iterative Training	-
CTF-MM	37.8	Connectionist Temporal Fusion for Sign Language Translation	-
C2SLR	20.4	C2SLR: Consistency-Enhanced Continuous Sign Language Recognition	-
TwoStream-SLR	18.4	Two-Stream Network for Sign Language Recognition and Translation
MSKA-SLR	22.1	Multi-Stream Keypoint Attention Network for Sign Language Recognition and Translation
CorrNet + VAC + SMKD	19.4	Continuous Sign Language Recognition with Correlation Network
Stochastic CSLR	25.3	Stochastic Fine-grained Labeling of Multi-state Sign Glosses for Continuous Sign Language Recognition	-
SlowFastSign	18.3	SlowFast Network for Continuous Sign Language Recognition
DTN	36.5	Dense Temporal Convolution Network for Sign Language Translation	-
CrossModal	24.0	Continuous Sign Language Recognition Through Cross-Modal Alignment of Video and Text Embeddings in a Joint-Latent Space	-
SMKD	20.5	Self-Mutual Distillation Learning for Continuous Sign Language Recognition	-
STMC	20.7	Spatial-Temporal Multi-Cue Network for Continuous Sign Language Recognition	-
TCNet	18.9	TCNet: Continuous Sign Language Recognition from Trajectories and Correlated Regions
RadialCTC	20.2	Deep Radial Embedding for Visual Sequence Learning	-

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