Recognizing Persian Automobile license plates under adverse rainy conditions
Paper ID : 1134-MVIP2020
Hossein Rezaei *, Maryam Haghshenas, Mahboobehsadat Yasini
This study is focused on identifying Persian license plate of Iranian cars in different rain conditions, with different distances and lighting, with simple and complex backgrounds and different angles of stationary cars. A method that is applicable to automated license plate identification systems, which is a type of intelligent transportation system. Systems that have been localized due to the variety of appearance of car license plates in different countries are currently being researched in many countries. Among the important challenges in identifying a vehicle license plate are inappropriate conditions such as adverse weather conditions such as rainy weather, snow, fog and dust, which make it difficult to identify license plates. The proposed method, which is a simple yet efficient method, employs many image processing techniques and morphology operations, and the results of implementing the proposed algorithm in MATLAB 2019b software on 420 Color image of car under low rainfall conditions, moderate rainfall and severe rainfall and storm show accuracy of 81%, 61.5% and 10.5% accuracy in identifying plaque IDs and their separation, respectively.
Automobile License Plate; Image processing; plate locating; separation and identification of identifier; morphology operations
Status : Paper Accepted (Poster Presentation)