Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Rethinking and Designing a High-performing Automatic License Plate
  Recognition Approach   In this paper, we propose a real-time and accurate automatic license plate
recognition (ALPR) approach. Our study illustrates the outstanding design of
ALPR with four insights: (1) the resampling-based cascaded framework is
beneficial to both speed and accuracy; (2) the highly efficient license plate
recognition should abundant additional character segmentation and recurrent
neural network (RNN), but adopt a plain convolutional neural network (CNN); (3)
in the case of CNN, taking advantage of vertex information on license plates
improves the recognition performance; and (4) the weight-sharing character
classifier addresses the lack of training images in small-scale datasets. Based
on these insights, we propose a novel ALPR approach, termed VSNet.
Specifically, VSNet includes two CNNs, i.e., VertexNet for license plate
detection and SCR-Net for license plate recognition, integrated in a
resampling-based cascaded manner. In VertexNet, we propose an efficient
integration block to extract the spatial features of license plates. With
vertex supervisory information, we propose a vertex-estimation branch in
VertexNet such that license plates can be rectified as the input images of
SCR-Net. In SCR-Net, we introduce a horizontal encoding technique for
left-to-right feature extraction and propose a weight-sharing classifier for
character recognition. Experimental results show that the proposed VSNet
outperforms state-of-the-art methods by more than 50% relative improvement on
error rate, achieving > 99% recognition accuracy on CCPD and AOLP datasets with
149 FPS inference speed. Moreover, our method illustrates an outstanding
generalization capability when evaluated on the unseen PKUData and CLPD
datasets.

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The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
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The categories of this paper are: mtrl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: adap-org
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The categories of this paper are: chao-dyn
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The categories of this paper are: math
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-ex
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The categories of this paper are: quant-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an
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The categories of this paper are: gr-qc
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The categories of this paper are: eess
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The categories of this paper are: math-ph