Fast and adaptive license plate recognition algorithm for Persian plates

Abstract

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.