Abstract
Most existing Automatic License Plate Recognition (ALPR) approaches focus on images containing approximately frontal views. The considerable variation of LP across complicated environments and perspectives remains a massive challenge for a robust ALPR. This work proposes a comprehensive ALPR paradigm emphasizing unrestricted express screenplays in which the LP may be significantly influenced by diverse shooting angles, illumination circumstances, and complicated surroundings. This system integrates a Spatial Transformer Network, which can catch and repair numerous distorted LPs in an image so that all the plates are consistently aligned. Then, a convolutional neural network is sketched to determine LP characters containing various font styles and sizes. We evaluated the system with a data set containing annotations for a challenging LP image set from multiple areas and acquisition states. The experimental outcomes reveal that our proposed ALPR paradigm attains adequate recognition accuracy compared to existing methods.
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Bakshi, A., Gulhane, S., Sawant, T., Sambhe, V., Udmale, S.S. (2023). ALPR - An Intelligent Approach Towards Detection and Recognition of License Plates in Uncontrolled Environments. In: Molla, A.R., Sharma, G., Kumar, P., Rawat, S. (eds) Distributed Computing and Intelligent Technology. ICDCIT 2023. Lecture Notes in Computer Science, vol 13776. Springer, Cham. https://doi.org/10.1007/978-3-031-24848-1_18
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