Sign Language Recognition Using Convolutional Neural Networks

By Lionel Pigou, Sander Dieleman, Pieter-Jan Kindermans, Benjamin Schrauwen. ECCV Workshops, 2014

476 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d0d34c673bd351d2e162469efc6245bf1fc43d33

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Citations in the corpus

Explore Efficient Local Features from RGB-D Data for One-Shot Learning Gesture Recognition (2016, background): “VISION-BASED gesture recognition [1] is a very important and active field of computer vision research due to a lot of varied applications, such as sign language recognition [2], human computer interaction (HCI) [3], robot control [4], video surveillance [5], augmented reality [6] and video annotatio”
Motion Recognition Employing Multiple Kernel Learning of Fisher Vectors Using Local Skeleton Features (2015, unknown): “[15] Depth, RGB 0.”
Large-scale Isolated Gesture Recognition using pyramidal 3D convolutional networks (2016, background): “Human gestures may involve motions of the whole body, but arms and hands play the crucial roles, especially for sign language recognition [5].”
Hand gesture recognition with 3D convolutional neural networks (2015, background): “Several authors have emphasized the importance of using many diverse training examples for CNNs [8, 17, 19].”
Multi-scale Deep Learning for Gesture Detection and Localization (2014, unknown): “[46] 0.”
A Deep Neural Framework for Continuous Sign Language Recognition by Iterative Training (2019, background): “Convolutional neural networks (CNNs) [25], [26] and 3D convolutional neural networks (3D-CNNs) [4], [9], [10] have also been em-”
Continuous sign language recognition: Towards large vocabulary statistical recognition systems handling multiple signers (2015, background): “[54] Pigou, L.”