In this paper, a license plate extraction algorithm is proposed, which can be used in moving vehicles. First, the Haar cascade classifier was used to find candidate regions. Then, a DoG filter was used to detect the edges and connected component labeling was applied to obtain the candidate blocks. The license plate color characteristics were used to eliminate irrelevant blocks using histogram comparison and color quantization. The Bhattacharyya distance and the correlation metric were used to compare the histograms. Experiments with real data showed good performance. The dataset consists of various road and weather conditions including expressway, downtown, sunny days and rainy days. For our dataset, the recall was 0.72, the precision was 0.88 and the F-score was 0.79. For the Caltech dataset, the recall was 0.86, the precision was 0.96 and the F-score was 0.91.
|Title of host publication||10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2019 Jul|
|Event||10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019 - Patras, Greece|
Duration: 2019 Jul 15 → 2019 Jul 17
|Name||10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019|
|Conference||10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019|
|Period||19/7/15 → 19/7/17|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1D1A1B07050345).
© 2019 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Information Systems
- Information Systems and Management
- Media Technology