A configurable method for multi-style license plate recognition

Abstract

Despite the success of license plate recognition (LPR) methods in the past decades, few of them can process multi-style license plates (LPs), especially LPs from different nations, effectively. In this paper, we propose a new method for multi-style LP recognition by representing the styles with quantitative parameters, i.e., plate rotation angle, plate line number, character type and format. In the recognition procedure these four parameters are managed by relevant algorithms, i.e., plate rotation, plate line segmentation, character recognition and format matching algorithm, respectively. To recognize special style LPs, users can configure the method by defining corresponding parameter values, which will be processed by the relevant algorithms. In addition, the probabilities of the occurrence of every LP style are calculated based on the previous LPR results, which will result in a faster and more precise recognition. Various LP images were used to test the proposed method and the results proved its effectiveness.

Introduction

Automatic license plate recognition (LPR) has been a practical technique in the past decades [1]. Numerous applications, such as automatic toll collection [1], criminal pursuit [2] and traffic law enforcement [3], have been benefited from it [1], [2], [3], [4], [5], [6], [7], [8], [9], [10]. Although some novel techniques, for example RFID (radio frequency identification), WSN (wireless sensor network), etc., have been proposed for car ID identification, LPR on image data is still an indispensable technique in current intelligent transportation systems for its convenience and low cost.

LPR is generally divided into three steps: license plate detection, character segmentation and character recognition. The detection step roughly classifies LP and non-LP regions, the segmentation step separates the symbols/characters from each other in one LP so that only accurate outline of each image block of characters is left for the recognition, and the recognition step finally converts grey-level image block into characters/symbols by pre-defined recognition models.

Although LPR technique has a long research history, it is still driven forward by various arising demands, the most frequent one of which is the variation of LP styles, for example:

• Appearance variation caused by the change of image capturing conditions.

• Style variation from one nation to another.

• Style variation when the government releases new LP format.

We summed them up into four factors, namely rotation angle, line number, character type and format, after comprehensive analyses of multi-style LP characteristics on real data. Generally speaking, any change of the above four factors can result in the change of LP style or appearance and then affect the detection, segmentation or recognition algorithms. If one LP has a large rotation angle, the segmentation and recognition algorithms for horizontal LP may not work. If there are more than one character lines in one LP, additional line separation algorithm is needed before a segmentation process. With the variation of character types when we apply the method from one nation to another, the ability to re-define the recognition models is needed. What is more, the change of LP styles requires the method to adjust by itself so that the segmented and recognized character candidates can match best with an LP format.

Several methods have been proposed for multi-national LPs or multi-format LPs in the past years [11], [12] while few of them comprehensively address the style adaptation problem in terms of the above-mentioned factors. Some of them only claim the ability of processing multi-national LPs by re-defining the detection and segmentation rules or recognition models.

In this paper, we propose a configurable LPR method which is adaptable from one style to another, particularly from one nation to another, by defining the four factors as parameters. Users can constrain the scope of a parameter and at the same time the method will adjust itself so that the recognition can be faster and more accurate. Similar to existing LPR techniques, we also provide details of LP detection, segmentation and recognition algorithms. The difference is that we emphasize on the configurable framework for LPR and the extensibility of the proposed method for multi-style LPs instead of the performance of each algorithm.

The rest of this paper is organized as follows. Related work is reviewed in Section 2. The configurable method is proposed in Section 3. Implementation of the configurable method is presented in Section 4. Experimental results are provided and discussed in Section 5 and conclusions in Section 6.