# Exploiting Recurrent Neural Networks and Leap Motion Controller for Sign Language and Semaphoric Gesture Recognition

arXiv: https://arxiv.org/abs/1803.10435 · HTML: https://mlsanity.com/paper/1803.10435/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2018.

## Built on by (most cited first, 9 of 9)

- [Multi-Modality Co-Learning for Efficient Skeleton-based Action Recognition](https://mlsanity.com/paper/2407.15706/) (2024)
- [A Two-stream Neural Network for Pose-based Hand Gesture Recognition](https://mlsanity.com/paper/2101.08926/) (2021)
- [A Methodological and Structural Review of Hand Gesture Recognition Across Diverse Data Modalities](https://mlsanity.com/paper/2408.05436/) (2024)
- [GestFormer: Multiscale Wavelet Pooling Transformer Network for Dynamic Hand Gesture Recognition](https://mlsanity.com/paper/2405.11180/) (2024)
- [HAN: An Efficient Hierarchical Self-Attention Network for Skeleton-Based Gesture Recognition](https://mlsanity.com/paper/2106.13391/) (2021)
- [3D Hand Pose and Shape Estimation from RGB Images for Keypoint-Based Hand Gesture Recognition](https://mlsanity.com/paper/2109.13879/) (2021)
- [OO-dMVMT: A Deep Multi-view Multi-task Classification Framework for Real-time 3D Hand Gesture Classification and Segmentation](https://mlsanity.com/paper/2304.05956/) (2023)
- [Multiscaled Multi-Head Attention-based Video Transformer Network for Hand Gesture Recognition](https://mlsanity.com/paper/2501.00935/) (2025)
- [Serial-parallel Multi-Scale Feature Fusion for Anatomy-Oriented Hand Joint Detection](https://mlsanity.com/paper/2102.09757/) (2021)

## Abstract (arXiv)

In human interactions, hands are a powerful way of expressing information that, in some cases, can be used as a valid substitute for voice, as it happens in Sign Language. Hand gesture recognition has always been an interesting topic in the areas of computer vision and multimedia. These gestures can be represented as sets of feature vectors that change over time. Recurrent Neural Networks (RNNs) are suited to analyse this type of sets thanks to their ability to model the long term contextual information of temporal sequences. In this paper, a RNN is trained by using as features the angles formed by the finger bones of human hands. The selected features, acquired by a Leap Motion Controller (LMC) sensor, have been chosen because the majority of human gestures produce joint movements that generate truly characteristic corners. A challenging subset composed by a large number of gestures defined by the American Sign Language (ASL) is used to test the proposed solution and the effectiveness of the selected angles. Moreover, the proposed method has been compared to other state of the art works on the SHREC dataset, thus demonstrating its superiority in hand gesture recognition accuracy.