Name
Abstract | ||
|---|---|---|
Ship plate recognition is challenging due to variations of plate locations and text types. This paper proposes an effcient Fully Convolutional Network based Plate Recognition approach FCNPR, which uses a CNN (Convolutional Neural Network) to locate ships, then detects plate text lines with the fully convolutional network (FCN). The recognition accuracy is improved with integrating the AIS (Automatic Identification System) information. The actual FCNPR deployment demonstrates that it can work reliably with a high accuracy for satisfying practical usages. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/SMC.2018.00312 | 2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) |
Keywords | Field | DocType |
Ship license plate recognition, Text detection, Fully Convolutional Network, AIS | Software deployment,Pattern recognition,Computer science,Convolutional neural network,Artificial intelligence,Automatic Identification System,Text detection,Machine learning | Conference |
ISSN | Citations | PageRank |
1062-922X | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haoyun Sun | 1 | 1 | 2.38 |
| Xin Liu | 2 | 82 | 12.27 |
| Guizhi Min | 3 | 0 | 0.34 |
| Jiehan Zhou | 4 | 226 | 28.61 |
| Weishan Zhang | 5 | 31 | 5.55 |
| Guizhi Min | 6 | 1 | 1.03 |