# Revolutionizing Communication with Deep Learning and XAI for Enhanced Arabic Sign Language Recognition

arXiv: https://arxiv.org/abs/2501.08169 · HTML: https://mlsanity.com/paper/2501.08169/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 44 references that are in the map.
- First posted on arXiv 14 Jan 2025.

## Builds on (most cited first, 7 of 7)

- [Advancing Spiking Neural Networks towards Deep Residual Learning](https://mlsanity.com/paper/2112.08954/) (2021)
- [Deep Network Approximation: Beyond ReLU to Diverse Activation Functions](https://mlsanity.com/paper/2307.06555/) (2023)
- [Optimal Ratio for Data Splitting](https://mlsanity.com/paper/2202.03326/) (2022)
- [Effects of Image Size on Deep Learning](https://mlsanity.com/paper/2101.11508/) (2021)
- [Balanced Split: A new train-test data splitting strategy for imbalanced datasets](https://mlsanity.com/paper/2212.11116/) (2022)
- [SwishReLU: A Unified Approach to Activation Functions for Enhanced Deep Neural Networks Performance](https://mlsanity.com/paper/2407.08232/) (2024)
- [Advanced Arabic Alphabet Sign Language Recognition Using Transfer Learning and Transformer Models](https://mlsanity.com/paper/2410.00681/) (2024)

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

- [Why Current XAI Is Not Enough for Arabic NLP: A Critical Survey of the Explainability Gap](https://mlsanity.com/paper/2608.26144/) (2026)

## Abstract (arXiv)

This study introduces an integrated approach to recognizing Arabic Sign Language (ArSL) using state-of-the-art deep learning models such as MobileNetV3, ResNet50, and EfficientNet-B2. These models are further enhanced by explainable AI (XAI) techniques to boost interpretability. The ArSL2018 and RGB Arabic Alphabets Sign Language (AASL) datasets are employed, with EfficientNet-B2 achieving peak accuracies of 99.48% and 98.99%, respectively. Key innovations include sophisticated data augmentation methods to mitigate class imbalance, implementation of stratified 5-fold cross-validation for better generalization, and the use of Grad-CAM for clear model decision transparency. The proposed system not only sets new benchmarks in recognition accuracy but also emphasizes interpretability, making it suitable for applications in healthcare, education, and inclusive communication technologies.