# Sign Language Fingerspelling Classification from Depth and Color Images using a Deep Belief Network

arXiv: https://arxiv.org/abs/1503.05830 · HTML: https://mlsanity.com/paper/1503.05830/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2015.

## Built on by (most cited first, 4 of 4)

- [Enhancing ResNet Image Classification Performance by using Parameterized Hypercomplex Multiplication](https://mlsanity.com/paper/2301.04623/) (2023)
- [Vision-Based American Sign Language Classification Approach via Deep Learning](https://mlsanity.com/paper/2204.04235/) (2022)
- [Handshape recognition for Argentinian Sign Language using ProbSom](https://mlsanity.com/paper/2310.17427/) (2023)
- [Stack Transformer Based Spatial-Temporal Attention Model for Dynamic Sign Language and Fingerspelling Recognition](https://mlsanity.com/paper/2503.16855/) (2025)

## Abstract (arXiv)

Automatic sign language recognition is an open problem that has received a lot of attention recently, not only because of its usefulness to signers, but also due to the numerous applications a sign classifier can have. In this article, we present a new feature extraction technique for hand pose recognition using depth and intensity images captured from a Microsoft Kinect sensor. We applied our technique to American Sign Language fingerspelling classification using a Deep Belief Network, for which our feature extraction technique is tailored. We evaluated our results on a multi-user data set with two scenarios: one with all known users and one with an unseen user. We achieved 99% recall and precision on the first, and 77% recall and 79% precision on the second. Our method is also capable of real-time sign classification and is adaptive to any environment or lightning intensity.