A fast and adaptive license plate localization algorithm with pattern-checking capabilities

A new license plate localization algorithm is presented. Execution times of these operations can rather be long, especially where the image consists of large amount of either vehicle's linked components or the other existing objects. This algorithm combines the image processing techniques with some statistical methods and eventually a pattern checking method is also added. Here, minimum rectangle bounding box has been used instead of common bounding box methods, detaching essential details out of blobs and performance improvement, combined with a defined quantity called license plate possibility ratio (LPPR) and standard deviation, we present a robust method of license plate localization. New way of finding license plate's location out of so many rectangles, considering “Sensitive to angle” conditions for characters has also been presented, specifically. It should be noted that the proposed algorithm is regardless of plate's location. This paper presents a different approach on thresholding utilization called “Dynamic Thresholding” which would be obtained by orderly scan of various and sequential ranges of threshold values, confronting probable drawbacks of image lighting caused by lack of light and brightness or another light source radiation, in which, the most desirable threshold value for detection procedure is unknown. Pattern checking phase consists of “Character-Separator” system, using predefined libraries, allows us to detect and specialize state or the city where the license plate's pattern is getting utilized. Presenting the best percent accuracy (95.33%) among relevant well-known algorithms, and also the 25ms run time of the program, would be strong proofs of algorithm's efficiency.