Using Deep Convolutional Networks for Gesture Recognition in American Sign Language
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Abstract (arXiv)
In the realm of multimodal communication, sign language is, and continues to be, one of the most understudied areas. In line with recent advances in the field of deep learning, there are far reaching implications and applications that neural networks can have for sign language interpretation. In this paper, we present a method for using deep convolutional networks to classify images of both the the letters and digits in American Sign Language.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Oct 2017.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 1.
4 papers.
- 2021Pose-Guided Sign Language Video GAN with Dynamic Lambdacited by 3
- 2022Efficient Gesture Recognition for the Assistance of Visually Impaired People using Multi-Head Neural Networkscited by 0
- 2023Live American Sign Language Letter Classification with Convolutional Neural Networkscited by 0
- 2024Enhancing Sign Language Detection through Mediapipe and Convolutional Neural Networks (CNN)cited by 0
- 2024Enhancing Sign Language Detection through Mediapipe and Convolutional Neural Networks (CNN)6 Jun 2024
- 2023Live American Sign Language Letter Classification with Convolutional Neural Networks26 May 2023
- 2022Efficient Gesture Recognition for the Assistance of Visually Impaired People using Multi-Head Neural Networks14 May 2022
- 2021Pose-Guided Sign Language Video GAN with Dynamic Lambda6 May 2021