# Sign Language Recognition using Bidirectional Reservoir Computing

arXiv: https://arxiv.org/abs/2512.00777 · HTML: https://mlsanity.com/paper/2512.00777/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 30 Nov 2025.

## Builds on (most cited first, 2 of 2)

- [Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison](https://mlsanity.com/paper/1910.11006/) (2019)
- [Sign Language Recognition Analysis using Multimodal Data](https://mlsanity.com/paper/1909.11232/) (2019)

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

- [Sign Language Recognition using Parallel Bidirectional Reservoir Computing](https://mlsanity.com/paper/2512.19451/) (2025)
- [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 individuals. Deep learning is widely used to develop SLR-based systems; however, it is computationally intensive and requires substantial computational resources, making it unsuitable for resource-constrained devices. To address this, we propose an efficient sign language recognition system using MediaPipe and an echo state network (ESN)-based bidirectional reservoir computing (BRC) architecture. MediaPipe extracts hand joint coordinates, which serve as inputs to the ESN-based BRC architecture. The BRC processes these features in both forward and backward directions, efficiently capturing temporal dependencies. The resulting states of BRC are concatenated to form a robust representation for classification. We evaluated our method on the Word-Level American Sign Language (WLASL) video dataset, achieving a competitive accuracy of 57.71% and a significantly lower training time of only 9 seconds, in contrast to the 55 minutes and 38 seconds required by the deep learning-based Bi-GRU approach. Consequently, the BRC-based SLR system is well-suited for edge devices.