Based on digital image processing techniques, the goal of this work is develop a method to automatically detect license plates. To achieve this goal, this thesis estimates the change of gray value on pixels to first find the position of the license plate. A number of image processing techniques are then developed to identify each character on the license plate. Finally, experimental results are given to demonstrate the effectiveness of the proposed approach, which is the followed by a simple conclusion.

In this thesis, we develop a method to automatically detect and recognize the vehicle license plates. By using a large number of images to study statistically several important features of the license plates, this work has developed several methods to systematically detect and recognize the license plates. In particular, these methods are used to detect edges, to locate regions with densely distributed edges, to detect region with grey color and to identify character shapes. This work restricts its study on outdoor environment. Many environmental uncertainties such as lighting and background complexity should also be considered. By taking these factors into consideration, our algorithm can first detect the license plates. Next, our system uses a two-stage approach to recognize the characters on the plates. Most of the characters can be correctly recognized in the first stages by using conventional template-based method. However, a moment-feature based method is applied to two pairs of characters which can not be accurately classified by the template-based method. Experimental results are given to demonstrate the effectiveness of the proposed approach. In order to improve the proposed approach in the future, this work also studies a relatively small portion of plates that can not be perfectly handled by the proposed approach.

The operation of a complete license plate recognition system includes three parts: license plate localization, character segmentation, and character identification. Among these three parts, license plate localization is relatively more difficult and complicated. Until now, differentiating background and real license plate images in real and random traffic conditions remains to be a very difficult task. Via a VQ coding technique, this study introduces a method resolve this problem. As a preprocessing step, this method first converts an image to be classified into binary form by using statistics generated from a license plate image database. The next step of the proposed approach is to use a VQ method to represent the image by a series of codewords. By computing the probability of these codewords used by the license plate and background images, these codewords are renumbered. By using neural networks to classify such images, our experimental results show that the proposed approach can differentiate background and real license plate images with a very high successful rate.

This paper studies the effects of auction mechanisms on the average price of auction results of Shanghai car license plate from 2004 to 2018. We construct two linear regression models and find that an iterated multi-unit auction has a lower efficiency than a seal-bid discriminatory multi-unit auction. We also find that the pre-set price-ceiling is positively correlated with the average winning price. These results suggest that the government can potentially manipulate auction results through the design of the auction mechanism, and through the setting of warning price as a price ceiling.

Based on digital image processing techniques, the goal of this work is develop a method to automatically detect license plates. To achieve this goal, this thesis uses wavelet transform to first find the position of the license plate. A number of image processing techniques are then developed to identify each character on the license plate. Finally, experimental results are given to demonstrate the effectiveness of the proposed approach, which is the followed by a simple conclusion.

License plate recognition (LPR) system plays an important role in numerous applications, such as parking accounting systems, traffic law enforcement, road monitoring, expressway toll system, electronic-police system, and security systems. In recent years, there has been a lot of research in license plate recognition, and many recognition systems have been proposed and used. But these systems have been developed for computers. In this project, we developed a mobile LPR system for Android Operating System (OS). LPR involves three main components: license plate detection, character segmentation and Optical Character Recognition (OCR). For License Plate Detection and character segmentation, we used JavaCV and OpenCV libraries. And for OCR, we used tesseract-ocr. We obtained very good results by using these libraries. We also stored records of license numbers in database and for that purpose SQLite has been used.

Object detection and machine learning are important fields in Computer science. This report presents two methods to find the bounding box of a license plate and tries to evaluate the best approach to deal with various lighting conditions. The first method uses edge detection to find a number of potential candidates, where each candidate is fed to a machine learning model who decides if the candidate is a license plate or not. This had an accuracy of 39%. This method is pointing towards struggling with varying light levels and the lowest accuracy was measured at the highest and lowest mean brightness values. The second method uses mostly machine learning to find the bounding box of a license plate which achieved a higher accuracy with 68%. This method seems to be better in low-light conditions and is more uniform in accuracy across different lighting conditions.

License plate recognition (LPR) technology has been widely applied in many different transportation applications such as enforcement, vehicle monitoring and access control. In most applications involving enforcement (e.g. cashless toll collection, congestion charging) and access control (e.g. car parking) a plate is recognized at one location (or checkpoint) and compared against a list of authorized vehicles. In this research I dealt with applications where a vehicle is detected at two locations and there is no list of reference for vehicle identification. There seems to be very little effort in the past to exploit all information generated by LPR systems. In nowadays, LPR machines have the ability to recognize most characters on the vehicle plates even under the harshest practical conditions. Therefore, even though the equipment are not perfect in terms of plate reading, it is still possible to judge with certain confidence if a pair of imperfect readings, in the form of sequenced characters (strings), most likely belong to the same vehicle. The challenge here is to design a matching procedure in order to decide whether or not they belong to same vehicle. In view of the aforementioned problem, this research intended to design and assess a matching procedure that takes advantage of a similarity measure called edit distance (ED) between two strings. The ED measure the minimum editing cost to convert a string to another. The study first attempted to assess a simple case of a dual LPR setup using the traditional ED formulation with 0 or 1 cost assignments (i.e. 0 if a pair-wise character is the same, and 1 otherwise). For this dual setup, this research has further proposed a symbol-based weight function using a probabilistic approach having as input parameters the conditional probability matrix of character association. As a result, this new formulation outperformed the original ED formulation. Lastly, the research sought to incorporate the passage time information into the procedure. With this, the performance of the matching procedure improved considerably resulting in a high positive matching rate and much lower (about 2%) false matching rate.

License Plate Recognition (LPR) systems are frequently utilized in various access controls and security applications. In this thesis, an experimental constraint based real-time License Plate Recognition system is designed, and implemented in Java platform. Many of the available constraint based methods worked under strict restrictions such as plate color, fixed illumination and designated routes, whereas, only the license plate geometry and format constraints are used in this developed system. These constraints are built on top of the current Turkish license plate regulations. The plate localization algorithm is based on vertical edge features where constraints are used to filter out non-text regions. Vertical and horizontal projections are used for character segmentation and Multi Layered Perceptron (MLP) based Optical Character Recognition (OCR) module has been implemented for character identification. The extracted license plate characters are validated against possible license plate formats during the recognition process. The system is tested both with Turkish and foreign license plate images including various plate orientation, image quality and size. An accuracy of 92% is achieved for license plate localization and %88 for character segmentation and recognition.

License plate recognition (LPR) technology has great importance for the development of Intelligent Transportation Systems by automatically identifying the vehicles using image processing and pattern recognition techniques. Conventional LPR systems consist of license plate detection (LPD), character segmentation (CS) and character recognition (CR) steps. Successful detection of license plate and character locations have vital role for proper LPR. Most LPD and CS techniques in the literature assume fixed distance and orientation from the vehicle to the imaging system. Hence, application areas of LPR systems using these techniques are limited to stationary platforms. However, installation of LPR systems on mobile platforms is required in many applications and algorithms that are invariant to distance, orientation, and illumination should be developed for this purpose. In this thesis work, a LPD algorithm that is based on multi-scale vertical edge density feature, and a character segmentation algorithm based on local thresholding and connected component analysis operations are proposed. Performance of the proposed algorithm is measured using ground truth positions of the license plate and characters. Algorithm parameters are optimized using recall and precision curves. Proposed techniques for each step give satisfying results for different license plate datasets and algorithm complexity is proper for real-time implementation if optimized.