International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Real-Time Vehicle License Plate Recognition (VLPR) Using Deep CNN

Author(s) Md. Hadiuzzaman Bappy, Kamrul Hasan Talukder
Country Bangladesh
Abstract This research introduces a dual-component Vehicle License Plate Recognition (VLPR) system designed to improve the accuracy and efficiency of automated traffic monitoring systems. The first component employs YOLOv8 for real-time detection of vehicle license plates, capitalizing on its advanced capabilities to effectively manage variations in environmental conditions and plate obfuscation. The second component, a custom Convolutional Neural Network (CNN), is optimized for high-precision character recognition from the detected plates. Trained on a dataset of over 33,000 images, the system achieves a detection accuracy of 97.30% and a character recognition accuracy of 98.10%, demonstrating its robustness and effectiveness. This integrated approach not only enhances the reliability of automated traffic monitoring but also holds significant promise for applications requiring high accuracy and real-time data processing across various operational settings.
Keywords YOLOv8, Convolutional Neural Network (CNN), Vehicle License Plate Recognition (VLPR), Real-time Processing, Accuracy
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-06-17
Cite This Real-Time Vehicle License Plate Recognition (VLPR) Using Deep CNN - Md. Hadiuzzaman Bappy, Kamrul Hasan Talukder - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.20897
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.20897
Short DOI https://doi.org/gt2cb4
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E-ISSN 2582-2160
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