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Towards End-to-end Car License Plate Location and Recognition in Unconstrained Scenarios
Benefiting from the rapid development of convolutional neural networks, the
performance of car license plate detection and recognition has been largely
improved. Nonetheless, challenges still exist especially for real-world
applications. In this paper, we present an efficient and accurate framework to
solve the license plate detection and recognition tasks simultaneously. It is a
lightweight and unified deep neural network, that can be optimized end-to-end
and work in real-time. Specifically, for unconstrained scenarios, an
anchor-free method is adopted to efficiently detect the bounding box and four
corners of a license plate, which are used to extract and rectify the target
region features. Then, a novel convolutional neural network branch is designed
to further extract features of characters without segmentation. Finally,
recognition task is treated as sequence labelling problems, which are solved by
Connectionist Temporal Classification (CTC) directly. Several public datasets
including images collected from different scenarios under various conditions
are chosen for evaluation. A large number of experiments indicate that the
proposed method significantly outperforms the previous state-of-the-art methods
in both speed and precision
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