Automatic license plate recognition (ALPR) has been the focus of many researches in the past years. In general, ALPR is divided into the following problems: detection of on-track vehicles, license plate detection, segmentation of license plate characters, and optical character recognition (OCR). Even though commercial solutions are available for controlled acquisition conditions, e.g., the entrance of a parking lot, ALPR is still an open problem when dealing with data acquired from uncontrolled environments, such as roads and highways when relying only on imaging sensors. Due to the multiple orientations and scales of the license plates captured by the camera, a very challenging task of the ALPR is the license plate character segmentation (LPCS) step, because its effectiveness is required to be (near) optimal to achieve a high recognition rate by the OCR. To tackle the LPCS problem, this work proposes a benchmark composed of a dataset designed to focus specifically on the character segmentation step of the ALPR within an evaluation protocol. Furthermore, we propose the Jaccard-centroid coefficient, an evaluation measure more suitable than the Jaccard coefficient regarding the location of the bounding box within the ground-truth annotation. The dataset is composed of 2000 Brazilian license plates consisting of 14000 alphanumeric symbols and their corresponding bounding box annotations. We also present a straightforward approach to perform LPCS efficiently. Finally, we provide an experimental evaluation for the dataset based on five LPCS approaches and demonstrate the importance of character segmentation for achieving an accurate OCR.
Many researchers have investigated automatic license plate recognition and its subtasks. Since this work focuses on the license plate character segmentation (LPCS) and on evaluation datasets, this section briefly reviews related works. We refer the reader to works such as @cite_20 @cite_40 for further information on the ALPR problem. The remaining of this section focuses on two aspects. First, we review works related to techniques of character segmentation. Then, we present works that propose character segmentation evaluation datasets used in different contexts. Finally, we present the works describing the techniques used as baselines in this work
@cite_40 @cite_20
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Automatic License Plate Recognition (ALPR) has important applications in traffic surveillance. It is a challenging problem especially in countries like in India where the license plates have varying sizes, number of lines, fonts etc. The difficulty is all the more accentuated in traffic videos as the cameras are placed high and most plates appear skewed. This work aims to address ALPR using Deep CNN methods for real-time traffic videos. We first extract license plate candidates from each frame using edge information and geometrical properties, ensuring high recall. These proposals are fed to a CNN classifier for License Plate detection obtaining high precision. We then use a CNN classifier trained for individual characters along with a spatial transformer network (STN) for character recognition. Our system is evaluated on several traffic videos with vehicles having different license plate formats in terms of tilt, distances, colors, illumination, character size, thickness etc. Results demonstrate robustness to such variations and impressive performance in both the localization and recognition. We also make available the dataset for further research on this topic. With the development of economy, more and more people can afford private cars. However, the traffic situation becomes more and more terrible because of the increase of cars in the city. To a large extent, Intelligent transport system (ITS) is proved to resolve this problem. License plate automatic recognition (LPR) is the key part of the ITS. Plate location, character segmentation and character identification are the three parts of LPR. Character segmentation is one of the important technologies of the License Plate Recognition. The paper mainly introduces the character segmentation algorithm based on vertical projection. We firstly analyses the principle of this method, and then verify the algorithm according to simulation in Matlab. The result of simulation shows that the algorithm can divide the character of plate effectively. The license plate character segmentation is one of the three key technologies of the automatic license plate recognition system and the character segmentation is the foundation of the character recognition.The large amount of calculations and a long time for processing are the drawbacks of traditional character segmentation algorithm based on the connected domain character segmentation algorithm,so this paper presents an improved algorithm,which makes full use of priori knowledge for character initial segmentation,and then realizes complete character segmentation based on the connected domain,meanwhile,this paper improves the traditional iteration binaryzation threshold algorithm by the priori knowledge,reducing iteration times.The experimental results show that the character segmentation algorithm provided by this paper greatly cuts down the processing time and meets real time requirement under the premise of accurate extraction of license plate character. This paper presents a vehicle license plate recognition method based on character-specific extremal regions (ERs) and hybrid discriminative restricted Boltzmann machines (HDRBMs). First, coarse license plate detection (LPD) is performed by top-hat transformation, vertical edge detection, morphological operations, and various validations. Then, character-specific ERs are extracted as character regions in license plate candidates. Followed by suitable selection of ERs, the segmentation of characters and coarse-to-fine LPD are achieved simultaneously. Finally, an offline trained pattern classifier of HDRBM is applied to recognize the characters. The proposed method is robust to illumination changes and weather conditions during 24 h or one day. Experimental results on thorough data sets are reported to demonstrate the effectiveness of the proposed approach in complex traffic environments.