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Found 9 papers, 1 papers with code
Transfer Learning for Cross-dataset Isolated Sign Language Recognition in Under-Resourced Datasets
Sign language recognition (SLR) has recently achieved a breakthrough in performance thanks to deep neural networks trained on large annotated sign datasets.
Conditional Information Gain Networks
These decision mechanisms are trained using cost functions based on differentiable Information Gain, inspired by the training procedures of decision trees.
BosphorusSign: A Turkish Sign Language Recognition Corpus in Health and Finance Domains
However, corpora collected for studying linguistic properties are often not suitable for sign language recognition as the statistical methods used in the field require large amounts of data.
Neural Sign Language Translation by Learning Tokenization
Sign Language Translation has attained considerable success recently, raising hopes for improved communication with the Deaf.
BosphorusSign22k Sign Language Recognition Dataset
Sign Language Recognition is a challenging research domain.
Temporal Accumulative Features for Sign Language Recognition
In this paper, we propose a set of features called temporal accumulative features (TAF) for representing and recognizing isolated sign language gestures.
Score-level Multi Cue Fusion for Sign Language Recognition
Therefore, Sign Language Recognition (SLR) needs to focus on all such cues.
The Multiscenario Multienvironment BioSecure Multimodal Database (BMDB)
It is comprised of more than 600 individuals acquired simultaneously in three scenarios: 1) over the Internet, 2) in an office environment with desktop PC, and 3) in indoor/outdoor environments with mobile portable hardware.
Conditional Information Gain Trellis
Conditional computing processes an input using only part of the neural network's computational units.
