# Temporal Accumulative Features for Sign Language Recognition

arXiv: https://arxiv.org/abs/2004.01225 · HTML: https://mlsanity.com/paper/2004.01225/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2020.

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

- [A Methodological and Structural Review of Hand Gesture Recognition Across Diverse Data Modalities](https://mlsanity.com/paper/2408.05436/) (2024)
- [Transfer Learning for Cross-dataset Isolated Sign Language Recognition in Under-Resourced Datasets](https://mlsanity.com/paper/2403.14534/) (2024)

## Abstract (arXiv)

In this paper, we propose a set of features called temporal accumulative features (TAF) for representing and recognizing isolated sign language gestures. By incorporating sign language specific constructs to better represent the unique linguistic characteristic of sign language videos, we have devised an efficient and fast SLR method for recognizing isolated sign language gestures. The proposed method is an HSV based accumulative video representation where keyframes based on the linguistic movement-hold model are represented by different colors. We also incorporate hand shape information and using a small scale convolutional neural network, demonstrate that sequential modeling of accumulative features for linguistic subunits improves upon baseline classification results.