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.
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
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.
Sign language translation (SLT) aims to interpret sign video sequences into text-based natural language sentences.
For that reason, we apply attention to synchronize and help capture entangled dependencies between the different sign language components.
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
We train CNNs for the classification of 31 alphabets and numbers using a subset of collected depth data from multiple subjects.
We propose a novel deep learning approach to solve simultaneous alignment and recognition problems (referred to as "Sequence-to-sequence" learning).