Sign Language Recognition
68 papers with code • 11 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
Latest papers
Slovo: Russian Sign Language Dataset
One of the main challenges of the sign language recognition task is the difficulty of collecting a suitable dataset due to the gap between hard-of-hearing and hearing societies.
ADDSL: Hand Gesture Detection and Sign Language Recognition on Annotated Danish Sign Language
Using this dataset, a one-stage ob-ject detector model (YOLOv5) was trained with the CSP-DarkNet53 backbone and YOLOv3 head to recognize letters (A-Z) and numbers (0-9) using only seven unique images per class (without augmen-tation).
Isolated Sign Language Recognition based on Tree Structure Skeleton Images
davidlainesv/sl-tssi-densenet
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We evaluated the effectiveness of our model on the Ankara University Turkish Sign Language (TSL) dataset, AUTSL, and a Mexican Sign Language (LSM) dataset.
Natural Language-Assisted Sign Language Recognition
Sign languages are visual languages which convey information by signers' handshape, facial expression, body movement, and so forth.
CVT-SLR: Contrastive Visual-Textual Transformation for Sign Language Recognition with Variational Alignment
binbinjiang/cvt-slr
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CVPR 2023
In this work, we propose a novel contrastive visual-textual transformation for SLR, CVT-SLR, to fully explore the pretrained knowledge of both the visual and language modalities.
Continuous Sign Language Recognition with Correlation Network
hulianyuyy/corrnet
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CVPR 2023
Visualizations demonstrate the effects of CorrNet on emphasizing human body trajectories across adjacent frames.
Fine-tuning of sign language recognition models: a technical report
ds-hub-sochi/sl-techreport
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We also investigated how the additional training of the model in another sign language affects the quality of recognition.
Improving Sign Recognition with Phonology
leekezar/modeling-asl-phonology
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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.
On the Importance of Sign Labeling: The Hamburg Sign Language Notation System Case Study
hearai/hearai
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Labeling is the cornerstone of supervised machine learning, which has been exploited in a plethora of various applications, with sign language recognition being one of them.
Learning from What is Already Out There: Few-shot Sign Language Recognition with Online Dictionaries
This dataset represents the actual distribution and characteristics of available online sign language data.
