# Action Recognition for American Sign Language

arXiv: https://arxiv.org/abs/2205.12261 · HTML: https://mlsanity.com/paper/2205.12261/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 5 references that are in the map.
- First posted on arXiv 20 May 2022.

## Builds on (most cited first, 1 of 1)

- [Offline and Online Deep Learning for Image Recognition](https://mlsanity.com/paper/1903.07479/) (2019)

## Built on by (most cited first, 2 of 2)

- [Video Action Recognition Collaborative Learning with Dynamics via PSO-ConvNet Transformer](https://mlsanity.com/paper/2302.09187/) (2023)
- [Extended KAFR: A kinematic-adaptive paradigm for the efficient analysis of surgical video](https://mlsanity.com/paper/2608.01058/) (2026)

## Abstract (arXiv)

In this research, we present our findings to recognize American Sign Language from series of hand gestures. While most researches in literature focus only on static handshapes, our work target dynamic hand gestures. Since dynamic signs dataset are very few, we collect an initial dataset of 150 videos for 10 signs and an extension of 225 videos for 15 signs. We apply transfer learning models in combination with deep neural networks and background subtraction for videos in different temporal settings. Our primarily results show that we can get an accuracy of 0.86 and 0.71 using DenseNet201, LSTM with video sequence of 12 frames accordingly.