Vehicle license plate detection and recognition

"December 2013.""A Thesis presented to the Faculty of the Graduate School at the University of Missouri In Partial Fulfillment of the Requirements for the Degree Master of Science."Thesis supervisor: Dr. Zhihai He.In this work, we develop a license plate detection method using a SVM (Support Vector Machine) classifier with HOG (Histogram of Oriented Gradients) features. The system performs window searching at different scales and analyzes the HOG feature using a SVM and locates their bounding boxes using a Mean Shift method. Edge information is used to accelerate the time consuming scanning process. Our license plate detection results show that this method is relatively insensitive to variations in illumination, license plate patterns, camera perspective and background variations. We tested our method on 200 real life images, captured on Chinese highways under different weather conditions and lighting conditions. And we achieved a detection rate of 100%. After detecting license plates, alignment is then performed on the plate candidates. Conceptually, this alignment method searches neighbors of the bounding box detected, and finds the optimum edge position where the outside regions are very different from the inside regions of the license plate, from color's perspective in RGB space. This method accurately aligns the bounding box to the edges of the plate so that the subsequent license plate segmentation and recognition can be performed accurately and reliably. The system performs license plate segmentation using global alignment on the binary license plate. A global model depending on the layout of license plates is proposed to segment the plates. This model searches for the optimum position where the characters are all segmented but not chopped into pieces. At last, the characters are recognized by another SVM classifier, with a feature size of 576, including raw features, vertical and horizontal scanning features. Our character recognition results show that 99% of the digits are successfully recognized, while the letters achieve an recognition rate of 95%. The license plate recognition system was then incorporated into an embedded system for parallel computing. Several TS7250 and an auxiliary board are used to simulIncludes bibliographical references (pages 67-73)

A vision-based machine learning method for barrier access control using vehicle license plate authentication

Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, the image is processed so that a region of interest is identified. In the recognition stage, features are extracted from the region of interest using the histogram of oriented gradients method. These features are then used to train an artificial neural network to identify characters in the license plate. Experimental results show that the proposed method achieves a high level of accuracy as well as low processing time when compared to existing methods, indicating that it is suitable for real-time applications

A Systematic Review of Vehicle License Plate Recognition Algorithms Based on Image Segmentation

Recently, vehicle license plate recognition (VLPR) is a very significant topic in smart transportation. License plate (LP) has become an important and difficult research problem in recent years due to its difficulties such as detection speed, noise, effects, various lighting, and others. In the same context, most VLPR algorithms include should have many methods to be able to identify LP images based on different letters, colors, languages, complex backgrounds, distortions, hazardous situations, obstructions, vehicle speeds, vertical or horizontal lines, horizontal slopes, and lighting.  Therefore, this study provides a comprehensive review of current VLPR algorithms in the context of detection, and segmentation. Where, available VLPR algorithms are classified according to image segmentation methods (characteristics, and features) and are compared in terms of simplicity, complexity, uptime, problems, and obstacles

An Efficient Traffic Control System and License Plate Detection Using Image Processing

Automatic license plate recognition is extracted from license plate of the vehicle. It is taken as an image or a continuous image taken in sequence. The extracted information can be with or without a database in many applications like electronic payment systems and freeway and arterial monitoring devices for traffic surveillance. ALPR employs CC camera, advanced camera or black and white, color camera to capture the image. ALPR is fruitful if the captured images are of good quality. ALPR is a real time application that processes the images of license plates in various conditions like dark or bright times in a day. A general technique should be identified to process images in many different countries or states. We should know that the license plate generally consists of various colors, languages, fonts and others have images in the background. Also, these plates are obstructed by mud, light, some accessories especially on a car. Here, we discuss about methods for ALPR. We classify ALPR based on the features they are used in each method and knowing their advantages, disadvantages, recognition accuracy and processing speed. Managing the timing in traffic controlling by calculating the density of an image

Incorporating negentropy in saliency-based search free car number plate localization

License plate localization algorithms aim to detect license plates within the scene. In this paper, a new algorithm is discussed where the necessary conditions are imposed into the saliency detection equations. Measures of distance between probability distributions such as negentropy finds the candidate license plates in the image and the Bayesian methodology exploits the a priori information to estimate the highest probability for each candidate. The proposed algorithm has been tested for three datasets, consisting of gray-scale and color images. A detection accuracy of 96% and an average execution time of 80 ms for the first dataset are the marked outcomes. The proposed method outperforms most of the state-of-the-art techniques and it is suitable to use in real-time ALPR applications