# Developing Lightweight DNN Models With Limited Data For Real-Time Sign Language Recognition

arXiv: https://arxiv.org/abs/2507.00248 · HTML: https://mlsanity.com/paper/2507.00248/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 8 references that are in the map.
- First posted on arXiv 30 Jun 2025.

## Builds on (most cited first, 3 of 3)

- [KD-MSLRT: Lightweight Sign Language Recognition Model Based on Mediapipe and 3D to 1D Knowledge Distillation](https://mlsanity.com/paper/2501.02321/) (2025)
- [GLoT: A Novel Gated-Logarithmic Transformer for Efficient Sign Language Translation](https://mlsanity.com/paper/2502.12223/) (2025)
- [A Comparative Study of Continuous Sign Language Recognition Techniques](https://mlsanity.com/paper/2406.12369/) (2024)

## Abstract (arXiv)

We present a novel framework for real-time sign language recognition using lightweight DNNs trained on limited data. Our system addresses key challenges in sign language recognition, including data scarcity, high computational costs, and discrepancies in frame rates between training and inference environments. By encoding sign language specific parameters, such as handshape, palm orientation, movement, and location into vectorized inputs, and leveraging MediaPipe for landmark extraction, we achieve highly separable input data representations. Our DNN architecture, optimized for sub 10MB deployment, enables accurate classification of 343 signs with less than 10ms latency on edge devices. The data annotation platform 'slait data' facilitates structured labeling and vector extraction. Our model achieved 92% accuracy in isolated sign recognition and has been integrated into the 'slait ai' web application, where it demonstrates stable inference.