TY  - JOUR
T1  - Significant Improvement in License Plate Recognition through Image Deduplication
AU  - Berrabah, Djamel
AU  - Gafour, Yacine
PY  - 2024
DA  - December
Y2  - 2024
DO  - 10.55549/epstem.1602777
JF  - The Eurasia Proceedings of Science Technology Engineering and Mathematics
JO  - EPSTEM
PB  - ISRES Publishing
WT  - DergiPark
SN  - 2602-3199
SP  - 295
EP  - 303
VL  - 32
LA  - en
AB  - 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.
KW  - Automatic number plate recognition (ANPR)
KW  - Perceptual hashing
KW  - Locality-sensitive hashing
(LSH)
KW  - Optical character recognition (OCR)
KW  - Duplicate image detection.
CR  - 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.
UR  - https://doi.org/10.55549/epstem.1602777
L1  - https://dergipark.org.tr/en/download/article-file/4446165
ER  -