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 works employ approaches based on Connected Component Analysis (CCA) and pixel projection techniques to tackle the LPCS problem. A study of the effectiveness of the CCA technique concluded that vertical projection can segment characters effectively @cite_33 . The segmentation approach proposed by Shapiro & Gluhchev @cite_18 utilizes an adaptive iterative thresholding approach to binarize the image and then segment the plate characters by employing a connected component analysis. @cite_3 proposed a method that uses ten samples of the same plate, binarize them, select the best one and then segment it using vertical projections. The work described in @cite_9 segments characters counting the black pixels in the horizontal and vertical direction of each license plate region. @cite_7 proposed a technique to segment the characters using CCA and evaluate it in databases manually and automatically cropped.
@cite_18 @cite_33 @cite_7 @cite_9 @cite_3
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Image-based car license plate recognition (CLPR) systems provide an inexpensive automatic solution for remote vehicle identification. Localization stage of the CLPR yields a gray-scale plate clip with printed characters. This paper describes the method of plate clip segmentation into isolated characters, feature extraction and classification. The method is independent on character size, thickness, illumination and is capable of handling plates from various countries. The method uses extensively the gray-scale information and is robust to breaks in character connectivity. It is tolerant to character deformations, such as shear and skew. Promising results have been obtained on Israeli and Bulgarian plates. 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. License Plate Detection and Recognition System is an image processing technique used to identify a vehicle by its license plate. Here we propose an accurate and robust method of license plate detection and recognition from an image using contour analysis. The system is composed of two phases: the detection of the license plate, and the character recognition. The license plate detection is performed for obtaining the candidate region of the vehicle license plate and determined using the edge based text detection technique. In the recognition phase, the contour analysis is used to recognize the characters after segmenting each character. The performance of the proposed system has been tested on various images and provides better results. Intelligent Traffic Systems (ITS) is an integral component of modern day road transportation networks. Identification of vehicles is one of the most important challenges to be addressed in the design of any intelligent traffic system. License plate being a unique identity for any registered vehicle, License Plate Recognition (LPR) systems have been used as the means to resolve the issue of identification of vehicles. Intelligent systems involving LPR has been widely applied in several applications such as intelligent traffic rule enforcement, prevention of car theft, intelligent traffic emergency accident handling, monitoring of vehicle traffic and flow control, intelligent parking, automated toll payment, intelligent surveillance and security enforcement etc. A typical LPR process consists of three stages viz; License Plate Extraction, Character Segmentation and Character Recognition. This paper address the Character Segmentation problem. The character segmentation method using horizontal and vertical projection and with dynaic thresholding is proposed in this paper. From the experimental studies made, it is learned that the proposed technique works reasonably well in real world scenarios.