License plate character identification method

Abstract

The invention provides a license plate character identification method. A descriptor adopted in the method directly takes the geometric center point of a character image as a characteristic point, and a main direction of characters in the license plate character image is sought by a principal component analysis (PCA) algorithm so that the descriptor of the character characteristic point is generated; compared with the conventional scale invariant feature transform (SIFT) descriptor, the calculation quantity of the main direction of the characters in the license plate character image is greatly simplified, the operation efficiency of a computer is improved, the real-time requirement on a license plate identification system can be better met, and the generated descriptor simultaneously ensures the rotation invariance of the license plate character image and capabilities of resisting noise and illumination influence and has good robustness; meanwhile, the combination of a support vector machine (SVM) classification algorithm and the SIFT descriptor is adopted for character identification of the license plate character image, so that compared with other classification algorithms adopting K nearest neighbors (KNN) and the like, the method has higher identification rate on the premise that the operation complexity of the classification algorithm is not increased.

Description

technical field [0001] The invention belongs to the technical field of traffic management and image recognition, and in particular relates to a character recognition method of a license plate. Background technique [0002] The license plate number is the same as the ID number to identify the vehicle. With the rapid development of intelligence in modern traffic management systems, license plate recognition systems are widely used in various fields of traffic management systems. The processing flow of the license plate recognition system usually includes five parts: image acquisition, image preprocessing, license plate image positioning, license plate character image segmentation, and license plate character recognition. Image information; the image preprocessing part can be performed before, between or after the two parts of the license plate image location and the license plate image segmentation. The identifiable performance of the license plate image; the license plate i...

AI Technical Summary

Problems solved by technology

Among them, the overall performance of the difference invariant descriptor (refer to "Koenderink J, van Doorn A J. Representation of local geo-ometry in the visual system. Biological Cybernetics, 1987, 55 (6): 367-375") is relatively high. Poor; adjustable filter (refer to "Freeman W T, Adelson EH. The design and use of steerable IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(9):891-906") and gradient moments (see "van Gool LJ, Moons T, Ungureanu D. Affne / photometric invariants for planar intensity patterns.In: Proceedings of the 4th European Conference on Computer Vision. Cam-bridge, England: Springer, 1996.642-651") Although the design is simple, the matching speed is fast, but the robustness is poor; the shape context (refer to the literature "Belongie S, Malik J, Puzicha J.Shape matching and ob-ject recognition using shape contexts.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(4):509-522"), complex coefficient filter (see "Schaffalitzky F, Zisserman A. Multi-view matching for un-ordered image sets, or 'How do I organize my holiday snaps?'.In: Processing of the 7th European Conference on Computer Vision. Copenhagen, Denmark: Springer, 2002.414-431") and other descriptor pairs The transformation of the image does not have good robustness; relatively speaking, the SIFT descriptor (Scale-invariant feature transform, scale-invariant feature transform, referred to as SIFT; refer to "Lowe D G. Distinctive image features from scale-invariant keypoints.International Journal of Computer Vision, 2004, 60(2):91-110") can be said to be the most robust descriptor at present, but due to the high computational complexity of its scale-invariant feature point extraction algorithm , which greatly improves the computer processing efficiency On the other hand, the 128-dimensional vector dimension in the SIFT descriptor is relatively high, which leads to a slow matching operation speed and further deteriorates the computer processing efficiency. Both of these reasons make it difficult for the SIFT descriptor to meet the license plate Real-time Requirements of Character Recognition System

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