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Smart city article details
| Title | Recognition System: Detection Of License Plate |
|---|---|
| ID_Doc | 44619 |
| Authors | Singh E.I.; Rahi P.; Jain V.; Sharma N.; Anand Y.; Shukla A.K. |
| Year | 2024 |
| Published | Proceedings of International Conference on Circuit Power and Computing Technologies, ICCPCT 2024 |
| DOI | http://dx.doi.org/10.1109/ICCPCT61902.2024.10673076 |
| Abstract | License Plate Recognition (LPR) systems offer increased efficiency, security, and automation across various domains, making them an essential tool for modern surveillance, management, and law enforcement efforts. In this paper, we present a new approach to LPR systems to solve the image ambiguity problem with high extraction and recognition accuracy. Integrating machine learning models like YOLO (You Only Look Once) V8 for region detection and convolutional neural networks (CNNs) for post-processing into a license plate recognition (LPR) system can significantly enhance its performance and accuracy. Using a dataset of approximately 8000 Indian license plate images for training and testing is a significant step in developing a robust license plate recognition (LPR) system tailored to Indian conditions, with accuracy of 92%. Image denoising as an integral part of the license plate recognition (LPR) system brings a novel dimension to the overall approach. Applying this to machine learning models can significantly improve the performance of LPR systems. The system uses advanced algorithms to correctly identify image components, allowing users to pivot on the relevant parts of the license plate. The utilization of CNNs further refines the extracted data, enabling precise recognition of alphanumeric characters despite image distortions. Following character extraction, the system undergoes a crucial verification process by cross-referencing the extracted data with the Indian Regional Transport Office (RTO) dataset. This verification step ensures the reliability and authenticity of the recognized license plate information. Remarkably, the proposed system achieves a verification accuracy of 96.5%, underscoring its efficacy in real world scenarios. The implications of this research extend beyond academic discourse, offering tangible benefits for modern transportation systems. By enhancing the accuracy and reliability of LPR systems, particularly in scenarios involving blurred or challenging images, the proposed methodology contributes to improved traffic management, enhanced security measures, and streamlined administrative processes. The ability to accurately identify and authenticate license plates in real-time has numerous practical applications across various sectors, particularly in law enforcement, transportation, and security. Moreover, the proposed system holds promise for integration into various smart city initiatives, where advanced technologies are leveraged to optimize urban infrastructure and enhance the overall quality of life for residents. It will be paid. This system is a comprehensive approach to solving problems caused by ambiguous images. Through the integration of reinvigorated machine learning models and dataset interpretation, the proposed system achieves high accuracy and reliability for practical applications in modern transportation systems and beyond. © 2024 IEEE. |
| Author Keywords | Denoiser; Image; License; Segmentation |
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