Name
Abstract | ||
|---|---|---|
License plate recognition is an essential step in automatic license plate recognition since it is a key technology to recognize detected license plates. Though there are extensive researches on license plate recognition, it is still challenging to recognize license plates under conditions like great tilt angles, uneven illuminations, and distortions. Based on the observation that an accurate shape correction can significantly improve the recognition accuracy on these images, this paper proposes a robust methodology named LCR for license plate recognition free of conventional image analysis operations. This approach is based on three neural networks for three different purposes: (i) predicting the locations of four vertices; (ii) predicting cutting locations; (iii) character classification. To the best of our knowledge, LCR is the first to address shape correction by designing neural networks to accurately predict the coordinates of license plates vertices. Experiments on over 250,000 unique images show that LCR significantly outperforms several state-of-the-art license plate recognition approaches. Moreover, in evaluations, the application of shape correction significantly improve the recognition accuracy. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICPR.2018.8546291 | 2018 24TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) |
Field | DocType | ISSN |
Computer vision,Pattern recognition,Character recognition,Computer science,Feature extraction,Image segmentation,Artificial intelligence,Artificial neural network,License | Conference | 1051-4651 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ajin Meng | 1 | 2 | 1.72 |
| Wei Yang | 2 | 1 | 2.71 |
| Zhenbo Xu | 3 | 3 | 4.77 |
| Huan Huang | 4 | 2 | 3.07 |
| Liusheng Huang | 5 | 473 | 64.55 |
| Changchun Ying | 6 | 0 | 0.34 |