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SotA/Computer Vision/Sign Language Recognition

Sign Language Recognition

27 benchmarks297 papers

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.

<span style="color:grey; opacity: 0.6">( Image credit: Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison )</span>

Benchmarks

Sign Language Recognition on RWTH-PHOENIX-Weather 2014

Word Error Rate (WER)

Sign Language Recognition on RWTH-PHOENIX-Weather 2014 T

Word Error Rate (WER)

Sign Language Recognition on CSL-Daily

Word Error Rate (WER)

Sign Language Recognition on AUTSL

Rank-1 Recognition Rate

Sign Language Recognition on WLASL-2000

Top-1 Accuracy

Sign Language Recognition on WLASL100

Top-1 AccuracyOfficial Test Split

Sign Language Recognition on LSA64

Accuracy (%)

Sign Language Recognition on ChicagoFSWild

CER (%)

Sign Language Recognition on ChicagoFSWild+

CER (%)

Sign Language Recognition on Znaki

CER (%)

Sign Language Recognition on MSASL-1000

P-I Top-1 AccuracyP-C Top-1 Accuracy

Sign Language Recognition on WLASL

Top-1 Accuracy

Sign Language Recognition on BOBSL

Actions Top-1

Sign Language Recognition on Bukva

Accuracy (Top-1)

Sign Language Recognition on FDMSE-ISL

Top-1 Accuracy

Sign Language Recognition on GSL

Rank-1 Recognition Rate

Sign Language Recognition on LIBRAS-UFOP

AccuracyF1-scorePrecisionRecall

Sign Language Recognition on MINDS-Libras

AccuracyF1-scorePrecisionRecall

Sign Language Recognition on Slovo: Russian Sign Language Dataset

Mean Accuracy