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.
 In this paper, a novel algorithm is proposed for segmentation of touching characters on the license plate. In our method, rough and precise segmentation of characters proceeds in sequence. At first, characters on the license plate are roughly classified as segmented and touching characters by vertical projection on the edge map. Next, segmentation points of touching characters are evaluated using fixed ratio relations of width, interval and height of characters. Finally, the touching characters are segmented by a path created by A* pathfinding algorithm. The proposed method is tested on 238 license plate images and the successful segmentation rate of 97.06 is achieved with only 62ms. And the experiment result demonstrates the effectiveness and efficient of our suggestion. 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 novel algorithm for license plate detection and license plate character segmentation problems by using the Gabor transform in detection and local vector quantization in segmentation. As of our knowledge this is the first application of Gabor filters to license plate segmentation problem. Even though much of the research efforts are devoted to the edge or global thresholding-based approaches, it is more practical and efficient to analyze the image in certain directions and scales utilizing the Gabor transform instead of error-prone edge detection or thresholding. Gabor filter response only gives a rough estimate of the plate boundary. Then binary split tree is used for vector quantization in order to extract the exact boundary and segment the plate region into disjoint characters which become ready for the optical character recognition.