# Sign Language Recognition using Parallel Bidirectional Reservoir Computing

arXiv: https://arxiv.org/abs/2512.19451 · HTML: https://mlsanity.com/paper/2512.19451/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 22 Dec 2025.

## Builds on (most cited first, 5 of 5)

- [A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning](https://mlsanity.com/paper/2307.15092/) (2023)
- [SignRep: Enhancing Self-Supervised Sign Representations](https://mlsanity.com/paper/2503.08529/) (2025)
- [Static Hand Gesture Recognition for American Sign Language using Neuromorphic Hardware](https://mlsanity.com/paper/2207.12559/) (2022)
- [Sign Language Recognition Analysis using Multimodal Data](https://mlsanity.com/paper/1909.11232/) (2019)
- [Sign Language Recognition using Bidirectional Reservoir Computing](https://mlsanity.com/paper/2512.00777/) (2025)

## Built on by (most cited first, 1 of 1)

- [A Low-Cost Hybrid Reservoir Computing Model for Isolated Sign Language Video Recognition](https://mlsanity.com/paper/2608.03444/) (2026)

## Abstract (arXiv)

Sign language recognition (SLR) facilitates communication between deaf and hearing communities. Deep learning based SLR models are commonly used but require extensive computational resources, making them unsuitable for deployment on edge devices. To address these limitations, we propose a lightweight SLR system that combines parallel bidirectional reservoir computing (PBRC) with MediaPipe. MediaPipe enables real-time hand tracking and precise extraction of hand joint coordinates, which serve as input features for the PBRC architecture. The proposed PBRC architecture consists of two echo state network (ESN) based bidirectional reservoir computing (BRC) modules arranged in parallel to capture temporal dependencies, thereby creating a rich feature representation for classification. We trained our PBRC-based SLR system on the Word-Level American Sign Language (WLASL) video dataset, achieving top-1, top-5, and top-10 accuracies of 60.85%, 85.86%, and 91.74%, respectively. Training time was significantly reduced to 18.67 seconds due to the intrinsic properties of reservoir computing, compared to over 55 minutes for deep learning based methods such as Bi-GRU. This approach offers a lightweight, cost-effective solution for real-time SLR on edge devices.