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Light-Weight Deep Learning Techniques with Advanced Processing for Real-Time Hand Gesture Recognition.

Mohamed S Abdallah^1,2, Gerges H Samaan³, Abanoub R Wadie³

¹Department of Computer Engineering, Gachon University, Seongnam 1342, Republic of Korea.

Sensors (Basel, Switzerland)

|January 8, 2023

Summary

This summary is machine-generated.

This study introduces a lightweight deep learning system for real-time dynamic sign language recognition (DSLR) on mobile devices. The system achieves high accuracy, bridging communication gaps for the hearing-impaired community.

Keywords:

1D CNN DSL-46 GRU MediaPipe hand gesture hand landmarks sign language

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Area of Science:

Computer Science
Artificial Intelligence
Human-Computer Interaction

Background:

Deep learning models for sign language recognition are computationally intensive, limiting mobile application development.
Existing methods struggle with resource constraints on mobile devices, hindering widespread adoption.

Purpose of the Study:

To develop a lightweight deep neural network for real-time dynamic sign language recognition (DSLR) on mobile phones.
To create a DSLR application that reduces the communication barrier between hearing-impaired and non-hearing individuals.

Main Methods:

Utilized a combination of Gated Recurrent Unit (GRU) and 1D Convolutional Neural Network (CNN) models.
Integrated the MediaPipe framework for efficient hand and pose landmark extraction.
Developed a novel dataset, DSL-46, comprising 46 daily American sign language signs.

Main Results:

The DSLR system demonstrated high accuracy across multiple datasets: 98.8% on DSL-46, 99.84% on LSA64, and 88.40% on LIBRAS-BSL.
Achieved fast and accurate real-time dynamic sign recognition, addressing challenges like depth and location variations.

Conclusions:

Lightweight deep neural networks offer a viable solution for real-time DSLR on resource-constrained mobile devices.
The developed DSLR application effectively enhances communication accessibility for the hearing-impaired community.