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arXiv · 1806.10447

LPRNet: License Plate Recognition via Deep Neural Networks

Sergey Zherzdev·Alexey Gruzdev
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Abstract

This paper proposes LPRNet - end-to-end method for Automatic License Plate Recognition without preliminary character segmentation. Our approach is inspired by recent breakthroughs in Deep Neural Networks, and works in real-time with recognition accuracy up to 95% for Chinese license plates: 3 ms/plate on nVIDIA GeForce GTX 1080 and 1.3 ms/plate on Intel Core i7-6700K CPU. LPRNet consists of the lightweight Convolutional Neural Network, so it can be trained in end-to-end way. To the best of our knowledge, LPRNet is the first real-time License Plate Recognition system that does not use RNNs. As a result, the LPRNet algorithm may be used to create embedded solutions for LPR that feature high level accuracy even on challenging Chinese license plates.

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Sergey Zherzdev, Alexey Gruzdev. 2018-06-27. LPRNet: License Plate Recognition via Deep Neural Networks. https://arxiv.org/abs/1806.10447

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