Vision-Based American Sign Language Classification Approach via Deep Learning
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Abstract (arXiv)
Hearing-impaired is the disability of partial or total hearing loss that causes a significant problem for communication with other people in society. American Sign Language (ASL) is one of the sign languages that most commonly used language used by Hearing impaired communities to communicate with each other. In this paper, we proposed a simple deep learning model that aims to classify the American Sign Language letters as a step in a path for removing communication barriers that are related to disabilities.
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References · 6
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2017Data Augmentation Generative Adversarial Networkscited by 278
- 2018How to Start Training: The Effect of Initialization and Architecturecited by 109
- 2019Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentationcited by 105
- 2013Some Improvements on Deep Convolutional Neural Network Based Image Classificationcited by 34
- 2015Sign Language Fingerspelling Classification from Depth and Color Images using a Deep Belief Networkcited by 4
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