Journal of Soft Computing Paradigm
IRO Journals
Volume 5 — Issue 4 — December 2023

CNN based System for Automatic Number Plate Recognition

https://doi.org/10.36548/jscp.2023.4.002
Gobinda Pandey
Department of Electronics and Computer Engineering, Institute of Engineering, Pashchimanchal Campus, Pokhara, Nepal
Karun K C
Department of Electronics and Computer Engineering, Institute of Engineering, Pashchimanchal Campus, Pokhara, Nepal
Nirajan Lamichhane
Department of Computer Science, St. Xavier’s College, Maitighar, Kathmandu,
Utsav Subedi
Department of Computer Science, St. Xavier’s College, Maitighar, Kathmandu, Nepal
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Pandey, Gobinda, Karun K C, Nirajan Lamichhane, and Utsav Subedi. 2024. “CNN Based System for Automatic Number Plate Recognition”. Journal of Soft Computing Paradigm 5 (4): 347-64. https://doi.org/10.36548/jscp.2023.4.002.
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Keywords

— Number Plate Detection
— Number Plate Recognition
— Image Processing
— Convolutional Neural Networks (CNNs)
— Digital Camera
— Bounding Box Method
— Character Segmentation.
Published: 09-01-2024

Abstract

This study presents a comprehensive approach to Automated Vehicle Number Plate Detection and Recognition, employing image processing and Convolutional Neural Networks (CNNs). The system encompasses two main stages: number plate detection and recognition. Utilizing a digital camera, the system employs image processing to segment the number plate region accurately. A super-resolution method is then applied via CNNs to enhance the image quality. Subsequently, a bounding box method isolates individual characters for precise recognition. In the recognition phase, CNNs extract features for effective classification. The study aims to advance automated vehicle identification systems for law enforcement and parking management applications, promising accurate and efficient number plate detection and recognition. The proposed work has also developed a user interface to ensure the successfulness of the objectives aimed.

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