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.

A new Persian license plate recognition algorithm is presented. These operations are highly susceptible to error, especially where the image consists of large amount of either vehicle's linked components or the other existing objects. Although the proposed character recognition procedure is highly optimized for Persian plates, the localization parts can be employed for all types of vehicles. Minimum rectangle bounding box is replaced the common bounding box methods, compensating normal bounding box's inherent flaws. License plate possibility ratio (LPPR) is a robust method proposed here to localize the plate. New method of finding plate's location out of so many rectangles, considering "Sensitive to angle" criterions for characters has also been presented. It should be noted that the process is regardless of the plate's location. Different approach on thresholding namely: "Dynamic Thresholding" is used to overcome the probable drawbacks caused by inappropriate lighting. From OCR point of view, a graph, consisting of two specifications will be formed and a set of rules will be defined to capture the character's label. An automated harassment section is added as the denoising filter, in order to omit the grinning ramifications. Presenting the best percent accuracy (95.33%) among relevant well-known algorithms in localization procedure with 25ms run time of the program, and also the outstanding results with over 97% of percent accuracy in character recognition of Persian plates with 30ms run time of the program on Linux and also average of 90ms on Android, can be listed as strong proofs of algorithm's efficiency.