Abstract
References
- Berrabah, D., & Gafour, Y. (2024). Significant improvement in license plate recognition through image deduplication. The Eurasia Proceedings of Science, Technology, Engineering & Mathematics (EPSTEM), 32, 295-303.
Abstract
The detection of license plates, or Automatic Number Plate Recognition (ANPR), is crucial for applications in parking management, vehicle tracking, and security. However, the efficiency of ANPR systems is often compromised by large datasets containing numerous similar or duplicate images, leading to increased storage costs and slowed processing times. This research proposes an innovative approach that combines perceptual hashing and locality-sensitive hashing (LSH) to enhance the detection of redundant images. Perceptual hashing generates unique visual fingerprints for images, facilitating efficient duplicate identification, while LSH groups similar images to reduce false positives. Additionally, Optical Character Recognition (OCR) is applied to the image pairs identified by LSH to extract license plates and verify vehicle identity. By integrating these techniques, the proposed method effectively mitigates redundancy, optimizing storage and improving the performance of ANPR systems for accurate real-world recognition.
Keywords
Automatic number plate recognition (ANPR) Perceptual hashing Locality-sensitive hashing (LSH) Optical character recognition (OCR) Duplicate image detection.
References
- Berrabah, D., & Gafour, Y. (2024). Significant improvement in license plate recognition through image deduplication. The Eurasia Proceedings of Science, Technology, Engineering & Mathematics (EPSTEM), 32, 295-303.
Details
| Primary Language | English |
|---|---|
| Subjects | Software Engineering (Other) |
| Journal Section | Articles |
| Authors | |
| Early Pub Date | December 16, 2024 |
| Publication Date | December 30, 2024 |
| Submission Date | May 6, 2024 |
| Acceptance Date | August 5, 2024 |
| Published in Issue | Year 2024 |
