Attention based unified architecture for Arabic text detection on traffic panels to advance autonomous navigation in natural scenes

The increasing reliance on autonomous navigation systems necessitates robust methods for detecting and recognizing textual information in natural scenes, especially in complex scripts like Arabic. This paper presents a novel attention-based unified architecture for Arabic text detection and recognit...

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Veröffentlicht in:Scientific reports Jg. 15; H. 1; S. 34138 - 22
Hauptverfasser: Hassan, Basma M., Gamel, Samah A., Talaat, Fatma M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 01.10.2025
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Schlagworte:
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Algorithms
Arabic text detection
Artificial intelligence
Attention-based architecture
Autonomous navigation
Autonomous vehicles
Computer vision
Datasets
Deep learning
Driver assistance systems
Handwriting
Humanities and Social Sciences
Large language models
Lighting
Long short-term memory
multidisciplinary
Neural networks
Plant diseases
Roads & highways
Science
Science (multidisciplinary)
Technological change
Traffic accidents & safety
Traffic panels
Transportation planning
Arabic text detection
Driver assistance systems
Autonomous navigation
Traffic panels
Attention-based architecture
ISSN:2045-2322, 2045-2322
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Zusammenfassung:The increasing reliance on autonomous navigation systems necessitates robust methods for detecting and recognizing textual information in natural scenes, especially in complex scripts like Arabic. This paper presents a novel attention-based unified architecture for Arabic text detection and recognition on traffic panels, addressing the unique challenges posed by Arabic’s cursive nature, varying character shapes, and contextual dependencies. Leveraging the ASAYAR dataset, which includes diverse Arabic text samples with precise annotations, the proposed model integrates Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks with attention mechanisms to accurately localize and interpret text regions. The architecture demonstrates state-of-the-art performance, achieving a Mean Intersection over Union (IoU) of 0.9505, precision of 0.953, recall of 0.934, F1-score of 0.929, and an overall recognition accuracy of 97%. Visualizations of attention weights and SHAP analyses highlight the model’s explainability and focus on relevant features, ensuring reliability in real-world applications. Furthermore, the system’s computational efficiency and real-time applicability make it suitable for use in Advanced Driver Assistance Systems (ADAS) and autonomous vehicles, reducing driver distractions and enhancing traffic safety. This study not only advances Arabic text recognition research but also provides insights into developing scalable, multilingual text detection systems for complex real-world scenarios.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-04326-4