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
R GeForceTMGTX
1080 and 1.3 ms/plate on Intel
R CoreTMi7-6700K 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
