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Toward a Vision-Based Intelligent System: A Stacked Encoded Deep Learning Framework for Sign Language Recognition.

Muhammad Islam¹, Mohammed Aloraini¹, Suliman Aladhadh²

¹Department of Electrical Engineering, College of Engineering, Qassim University, Unaizah 56452, Saudi Arabia.

Sensors (Basel, Switzerland)

|November 25, 2023

Summary

This summary is machine-generated.

This study introduces an AI and IoT-based stacked encoded model for improved sign language recognition. The novel approach significantly enhances accuracy and reduces computational costs for better accessibility.

Keywords:

Arabic sign language recognition computer vision convolution neural network deep learning image processing machine learning

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Area of Science:

Computer Science
Artificial Intelligence
Internet of Things

Background:

Sign language recognition systems struggle with high false positive rates and computational demands.
Existing deep learning methods require further optimization for practical applications.

Purpose of the Study:

To develop an efficient and accurate sign language recognition model using Artificial Intelligence (AI) and the Internet of Things (IoT).
To address limitations in feature extraction and classification accuracy in current sign language recognition technologies.

Main Methods:

A stacked encoded model incorporating a lightweight backbone for initial feature extraction.
Stacked autoencoders were employed for advanced feature refinement.
Integration of AI with IoT for scalable big data processing.

Main Results:

The proposed model demonstrated significant improvements in accuracy, precision, recall, and F1-score.
Complexity analysis indicated reduced computational costs compared to existing methods.
Superior performance was observed on the ArSL2018 benchmark dataset against state-of-the-art approaches.

Conclusions:

The AI-IoT stacked encoded model offers a robust solution for sign language recognition challenges.
The approach shows potential for sustainable development of high-performing, real-world applications.
Validation through ablation studies confirmed the model's efficacy across all metrics.