Weighted Edit Distance for Country Code Recognition in License Plates
Saved in:
| Title: | Weighted Edit Distance for Country Code Recognition in License Plates |
|---|---|
| Authors: | Kateryna Chumachenko, Alexandros Iosifidis, Moncef Gabbouj |
| Publisher Information: | Zenodo |
| Publication Year: | 2022 |
| Collection: | Zenodo |
| Subject Terms: | license plate recognition, edit distance, Leven- shtein distance, country code recognition |
| Description: | 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). |
| Document Type: | conference object |
| Language: | English |
| Relation: | https://zenodo.org/communities/marvel_project/; https://zenodo.org/communities/eu/; https://zenodo.org/records/6736842; oai:zenodo.org:6736842; https://doi.org/10.5281/zenodo.6736842 |
| DOI: | 10.5281/zenodo.6736842 |
| Availability: | https://doi.org/10.5281/zenodo.6736842 https://zenodo.org/records/6736842 |
| Rights: | Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode |
| Accession Number: | edsbas.E8DBD50B |
| Database: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | 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). |
|---|---|
| DOI: | 10.5281/zenodo.6736842 |