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Signer-Independent Arabic Sign Language Recognition System Using Deep Learning Model.

Kanchon Kanti Podder¹, Maymouna Ezeddin², Muhammad E H Chowdhury³

¹Department of Biomedical Physics & Technology, University of Dhaka, Dhaka 1000, Bangladesh.

Sensors (Basel, Switzerland)

|August 26, 2023

Summary

This summary is machine-generated.

This study introduces an Arabic Sign Language recognition system using CNN-LSTM-SelfMLP models. The best model achieved 87.69% accuracy, significantly improving upon previous methods for sign language interpretation.

Keywords:

Arabic Sign Language MediaPipe deep learning dynamic sign language segmentation

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Area of Science:

Computer Science
Artificial Intelligence
Human-Computer Interaction

Background:

Sign language is crucial for communication for hearing and speech-disabled individuals.
Interactions between sign language users and non-users present communication challenges.
Sign language recognition systems can bridge this communication gap.

Purpose of the Study:

To develop and evaluate a robust Arabic Sign Language recognition system.
To compare the performance of different CNN-LSTM-SelfMLP architectures.
To assess the effectiveness of face-hand region-based segmentation for improved recognition.

Main Methods:

Utilized two datasets: raw and face-hand region-based segmented RGB videos of Arabic Sign Language.
Proposed an operational layer-based multi-layer perceptron (SelfMLP).
Constructed six CNN-LSTM-SelfMLP models using MobileNetV2 and ResNet18 backbones with three SelfMLPs, tested in a signer-independent mode.

Main Results:

The MobileNetV2-LSTM-SelfMLP model on the segmented dataset achieved the highest accuracy (87.69%).
This model also demonstrated strong performance with 88.57% precision, 87.69% recall, 87.72% F1 score, and 99.75% specificity.
The face-hand region-based segmentation combined with the SelfMLP-infused MobileNetV2-LSTM-SelfMLP model showed a 10.970% accuracy improvement over prior research.

Conclusions:

Face-hand region-based segmentation is a key factor in enhancing Arabic Sign Language recognition accuracy.
The proposed SelfMLP integrated with CNN-LSTM architectures offers a promising approach for real-time sign language interpretation.
This system has the potential to significantly improve communication accessibility for Arabic sign language users.