Abstract
The goal of an Automatic License Plate Recognition (ALPR) system is to capture and recognize a vehicle license plate. This is an important computer vision problem and has number of application domains: law enforcement, public safety agencies, and toll gate systems to name a few. At the heart of ALPR systems is the character recognition system as it is a unique identifier for any given vehicle. We construct an ALPR character recognition system by creating a dataset to simulate a captured license plate image, applying multiple binarization techniques to segment the characters from state, from the plate and from each other and finally using this dataset to train a convolutional neural network.
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Angara, S., Robinson, M. (2020). License Plate Character Recognition Using Binarization and Convolutional Neural Networks. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-17795-9_19
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DOI: https://doi.org/10.1007/978-3-030-17795-9_19
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