LPRNet: License Plate Recognition via Deep Neural Networks
arXiv:1806.10447
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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- Practical License Plate Recognition in Unconstrained Surveillance Systems with Adversarial Super-Resolution
- Towards End-to-end Car License Plate Location and Recognition in Unconstrained Scenarios
- A Robust Attentional Framework for License Plate Recognition in the Wild
- GRCNN: Graph Recognition Convolutional Neural Network for Synthesizing Programs from Flow Charts
- Indian Licence Plate Dataset in the wild