Downloads provided by UsageCounts
This paper presents the problem of country code recognition from license plate images. We propose an approach based on character detection and subsequent clustering for country code localization. We further propose three weighted Edit Distance metrics for country of origin prediction from imperfect detections, namely based on character similarity, detection confidence, and relative operation importance. Experimental results show the benefit of proposed approaches on real-world data. The proposed method is lightweight and independent of the underlying object detector, facilitating its application on edge devices.
This work was supported by Business Finland project 5G-VIIMA. A. Iosifidis acknowledges funding from the EU H2020 research and innovation program under grant agreement No. 957337 (MARVEL).
country code recognition, edit distance, 113 Computer and information sciences, license plate recognition, Leven- shtein distance
country code recognition, edit distance, 113 Computer and information sciences, license plate recognition, Leven- shtein distance
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 1 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 8 | |
| downloads | 15 |

Views provided by UsageCounts
Downloads provided by UsageCounts