Papers › LPRNet: License Plate Recognition via Deep Neural Networks
LPRNet: License Plate Recognition via Deep Neural Networks
Sergey Zherzdev, Alexey Gruzdev
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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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| License Plate Recognition | Chinese License Plates | LPRNet basic | GFLOPs | 0.34 | #1 of 3 | Archive leaderboard | report |
| License Plate Recognition | Chinese License Plates | LPRNet reduced | GFLOPs | 0.94 | #2 of 3 | Archive leaderboard | report |
| License Plate Recognition | Chinese License Plates | LPRNet baseline | Accuracy | 94.1 | #3 of 3 | Archive leaderboard | report |
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