LPRNet: License Plate Recognition via Deep Neural Networks
Sergey Zherzdev, Alexey Gruzdev
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/FaceOnLive/License-Plate-Recognition-SDK-Androidnone★ 25
- github.com/lyl8213/Plate_Recognition-LPRnettf★ 0
- github.com/SQMah/Plate-Reading-Networktf★ 0
- github.com/ZosoV/license-plate-recognitiontf★ 0
- github.com/tn00378077/licensesnone★ 0
- github.com/Tubaher/lprtf★ 0
- github.com/mesakarghm/LPRNETtf★ 0
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.