Abstract
Automatic license plate recognition (LPR) plays an important role in numerous applications and a number of techniques have been proposed. However, most of them worked under restricted conditions, such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. In this study, as few constraints as possible on the working environment are considered. The proposed LPR technique consists of two main modules: a license plate locating module and a license number identification module. The former characterized by fuzzy disciplines attempts to extract license plates from an input image, while the latter conceptualized in terms of neural subjects aims to identify the number present in a…
Citation impact
788
total citations
- FWCI
- 53.11
- Percentile
- 100%
- References
- 39
Citations per year
Authors
4Topics & keywords
Topics
Keywords
- License
- Identification (biology)
- Artificial intelligence
- Computer vision
- Computer science
- Image (mathematics)
- Pattern recognition (psychology)
No related works found for this paper.