# Vision-Based American Sign Language Classification Approach via Deep Learning

arXiv: https://arxiv.org/abs/2204.04235 · HTML: https://mlsanity.com/paper/2204.04235/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 8 Apr 2022.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Data Augmentation Generative Adversarial Networks](https://mlsanity.com/paper/1711.04340/) (2017)
- [How to Start Training: The Effect of Initialization and Architecture](https://mlsanity.com/paper/1803.01719/) (2018)
- [Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation](https://mlsanity.com/paper/1906.02611/) (2019)
- [Some Improvements on Deep Convolutional Neural Network Based Image Classification](https://mlsanity.com/paper/1312.5402/) (2013)
- [Sign Language Fingerspelling Classification from Depth and Color Images using a Deep Belief Network](https://mlsanity.com/paper/1503.05830/) (2015)

## 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.