Sign Language Recognition

34 papers with code • 6 benchmarks • 14 datasets

Given a signed video input the task is to predict the (sequence of) sign(s) that are performed.

( Image credit: Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison )

Most implemented papers

Learning to Estimate 3D Hand Pose from Single RGB Images

lmb-freiburg/hand3d ICCV 2017

Low-cost consumer depth cameras and deep learning have enabled reasonable 3D hand pose estimation from single depth images.

Fingerspelling recognition in the wild with iterative visual attention

chevalierNoir/asl-iter-attn ICCV 2019

In this paper we focus on recognition of fingerspelling sequences in American Sign Language (ASL) videos collected in the wild, mainly from YouTube and Deaf social media.

Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison

dxli94/WLASL 24 Oct 2019

Based on this new large-scale dataset, we are able to experiment with several deep learning methods for word-level sign recognition and evaluate their performances in large scale scenarios.

BlazePose: On-device Real-time Body Pose tracking

VNOpenAI/tf-blazepose 17 Jun 2020

We present BlazePose, a lightweight convolutional neural network architecture for human pose estimation that is tailored for real-time inference on mobile devices.

TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation

verashira/TSPNet NeurIPS 2020

Sign language translation (SLT) aims to interpret sign video sequences into text-based natural language sentences.

Context Matters: Self-Attention for Sign Language Recognition

faresbs/slrt 12 Jan 2021

For that reason, we apply attention to synchronize and help capture entangled dependencies between the different sign language components.

Skeleton Aware Multi-modal Sign Language Recognition

jackyjsy/CVPR21Chal-SLR 16 Mar 2021

Sign language is commonly used by deaf or speech impaired people to communicate but requires significant effort to master.

Sign Language Recognition via Skeleton-Aware Multi-Model Ensemble

jackyjsy/sam-slr-v2 12 Oct 2021

Current Sign Language Recognition (SLR) methods usually extract features via deep neural networks and suffer overfitting due to limited and noisy data.

Real-time Sign Language Fingerspelling Recognition using Convolutional Neural Networks from Depth map

byeongkeun-kang/FingerspellingRecognition 10 Sep 2015

We train CNNs for the classification of 31 alphabets and numbers using a subset of collected depth data from multiple subjects.

SubUNets: End-To-End Hand Shape and Continuous Sign Language Recognition

neccam/SubUNets ICCV 2017

We propose a novel deep learning approach to solve simultaneous alignment and recognition problems (referred to as "Sequence-to-sequence" learning).