Number plate coarse positioning method based on self-adaptation edge features

Abstract

The invention provides a number plate coarse positioning method based on self-adaptation edge features. The method includes the following steps that gray-level image fuzzy processing is conducted; the distance threshold value between adjacent points in a row is dynamically obtained by means of the self-adaptation edge features based on the density information of perpendicular edges, the adjacent points smaller than threshold value are connected, and communication areas are formed; non-plate-number communication areas are removed based on the basic feature information, including the height, the width, the aspect ratio and the area, of the communication area; corresponding expanding is conducted according to the edge information of the periphery of a candidate communication area, and a plate number candidate rectangular area is formed. According to the number plate coarse positioning method, more accurate communication areas can be obtained, more non-plate-number areas are removed, meanwhile, scale changes do not need to be conducted on the whole processing process for images including different sizes of number plates, corresponding positioning detection can be completed, and an algorithm is more efficient.

Description

Technical field [0001] The invention relates to the technical field of license plate recognition, in particular to a rough positioning method for license plate based on adaptive edge features. Background technique [0002] License plate recognition is the core technology of intelligent transportation, including three parts: license plate location, character segmentation, and character recognition. Among them, the license plate location is the most important part of the entire technology. The quality of the license plate location directly affects the subsequent steps, which in turn affects the overall recognition speed and accuracy. [0003] License plate location is to quickly find out the approximate area of ​​the license plate in an image. It is generally achieved by a combination of coarse positioning and fine positioning. The specific method is: coarse positioning quickly eliminates most of the non-license plate areas in the entire image, leaving candidate license plate areas...

AI Technical Summary

Problems solved by technology

But there are also a series of disadvantages: since the distance threshold between the morphological structural elements and the adjacent points of the row is mostly fixed, the adaptability to license plates of different sizes is poor, which leads to more false detections.

Some algorithms adopt the method of multiple detection after scale transformation. Although the effect of adapting to license plate images of different sizes is achieved, the repeated operation process greatly reduces the speed of the algorithm and seriously affects the final performance.

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