Automated License Plate Recognition using Existing University Infrastructure and Different Camera Angles

Number or license plate recognition has become an essential technology for traffic and security applications. Providing access control at any organization or academic institution improves the level of security. However, providing security personnel to manually control the access of vehicles at an academic institution is costly, time-consuming, and to a limited extent, error prone. This study investigated the use of an automated vehicle tracking system, incorporating experimental computer vision techniques for license plate recognition that runs in real-time to provide access control for vehicles and provide increased security for an academic institution. A vehicle monitoring framework was designed by using various technologies and experimenting with different camera angles. In addition, the effect of environmental changes on the accuracy of the optical character recognition application was assessed. The Design Science Research methodology was followed to develop the vehicle monitoring framework artifact. Image enhancement algorithms were tested, and the most viable options were evaluated and implemented. Optimal operating criteria that were established for the vehicle monitoring framework achieved a 96% success rate. The results indicate that a cost-effective solution could be provided by using an existing camera infrastructure at an academic institution and suitable license plate recognition software technologies, algorithms, and different camera angles

Optical character recognition on heterogeneous SoC for HD automatic number plate recognition system

Automatic number plate recognition (ANPR) systems are becoming vital for safety and security purposes. Typical ANPR systems are based on three stages: number plate localization (NPL), character segmentation (CS), and optical character recognition (OCR). Recently, high definition (HD) cameras have been used to improve their recognition rates. In this paper, four algorithms are proposed for the OCR stage of a real-time HD ANPR system. The proposed algorithms are based on feature extraction (vector crossing, zoning, combined zoning, and vector crossing) and template matching techniques. All proposed algorithms have been implemented using MATLAB as a proof of concept and the best one has been selected for hardware implementation using a heterogeneous system on chip (SoC) platform. The selected platform is the Xilinx Zynq-7000 All Programmable SoC, which consists of an ARM processor and programmable logic. Obtained hardware implementation results have shown that the proposed system can recognize one character in 0.63 ms, with an accuracy of 99.5% while utilizing around 6% of the programmable logic resources. In addition, the use of the heterogenous SoC consumes 36 W which is equivalent to saving around 80% of the energy consumed by the PC used in this work, whereas it is smaller in size by 95%

Real time mobile based license plate recognition system with neural networks

In this paper, the implementation of localizing and recognizing license plate in real time environment with a neural network using a mobile device is described. The neural networks used in this research are Convolutional Neural Network (CNN) and Backpropagation Feed Forward Neural Network (BPFFNN). Image processing algorithm for pre-processing, localization and segmentation is chosen based on its ability to cope with limited computational resource in mobile device. The proposed license plate localization steps include combination of Sobel edge detection method and morphological based method. Detected license plate image is segmented using connected component analysis (CCA) and bounding box method. Each cropped character is fed into CNN or BPFFNN model for character recognition process. The neural network model was pretrained using desktop computer and then later exported and implemented in Android mobile device. The experiment was conducted in a moving vehicle on selected driving routes. The results obtained showed that CNN performed better compared to BPFFNN in a real time environment

IoT-Driven Automated Object Detection Algorithm for Urban Surveillance Systems in Smart Cities

Automated object detection algorithm is an important research challenge in intelligent urban surveillance systems for Internet of Things (IoT) and smart cities applications. In particular, smart vehicle license plate recognition and vehicle detection are recognized as core research issues of these IoT-driven intelligent urban surveillance systems. They are key techniques in most of the traffic related IoT applications, such as road traffic real-time monitoring, security control of restricted areas, automatic parking access control, searching stolen vehicles, etc. In this paper, we propose a novel unified method of automated object detection for urban surveillance systems. We use this novel method to determine and pick out the highest energy frequency areas of the images from the digital camera imaging sensors, that is, either to pick the vehicle license plates or the vehicles out from the images. Our proposed method can not only help to detect object vehicles rapidly and accurately, but also can be used to reduce big data volume needed to be stored in urban surveillance systems

Big data analytics and processing for urban surveillance systems

Urban surveillance systems will be more demanding in the future towards smart city to improve the intelligence of cities. Big data analytics and processing for urban surveillance systems become increasingly important research areas because of infinite generation of massive data volumes all over the world. This thesis focused on solving several challenging big data issues in urban surveillance systems. First, we proposed several simple yet efficient video data recoding algorithms to be used in urban surveillance systems. The key idea is to record the important video frames when cutting the number of unimportant video frames. Second, since the DCT based JPEG standard encounters problems such as block artifacts, we proposed a very simple but effective method which results in better quality than widely used filters while consuming much less computer CPU resources. Third, we designed a novel filter to detect either the vehicle license plates or the vehicles from the images captured by the digital camera imaging sensors. We are the first to design this kind of filter to detect the vehicle/license plate objects. Fourth, we proposed novel grate filter to identify whether there are objects in these images captured by the cameras. In this way the background images can be updated from time to time when no object is detected. Finally, we combined image hash with our novel density scan method to solve the problem of retrieving similar duplicate images

Intelligent System for Vehicles Number Plate Detection and Recognition Using Convolutional Neural Networks

Vehicles on the road are rising in extensive numbers, particularly in proportion to the industrial revolution and growing economy. The significant use of vehicles has increased the probability of traffic rules violation, causing unexpected accidents, and triggering traffic crimes. In order to overcome these problems, an intelligent traffic monitoring system is required. The intelligent system can play a vital role in traffic control through the number plate detection of the vehicles. In this research work, a system is developed for detecting and recognizing of vehicle number plates using a convolutional neural network (CNN), a deep learning technique. This system comprises of two parts: number plate detection and number plate recognition. In the detection part, a vehicle’s image is captured through a digital camera. Then the system segments the number plate region from the image frame. After extracting the number plate region, a super resolution method is applied to convert the low-resolution image into a high-resolution image. The super resolution technique is used with the convolutional layer of CNN to reconstruct the pixel quality of the input image. Each character of the number plate is segmented using a bounding box method. In the recognition part, features are extracted and classified using the CNN technique. The novelty of this research is the development of an intelligent system employing CNN to recognize number plates, which have less resolution, and are written in the Bengali language.</jats:p

Detection and Recognition of License Plates by Convolutional Neural Networks

The current advancements in machine intelligence have expedited the process of recognizing vehicles and other objects on the roads. The License Plate Recognition system (LPR) is an open challenge for many researchers to develop a reliable and accurate system for automatic license plate recognition. Several methods including Deep Learning techniques have been proposed recently for LPR, yet those methods are limited to specific regions or privately collected datasets. In this thesis, we propose an end-to-end Deep Convolutional Neural Network system for license plate recognition that is not limited to a specific region or country. We apply a modified version of YOLO v2 to first recognize the vehicle and then localize the license plate. Moreover, through the convolutional procedures, we improve an Optical Character Recognition network (OCR-Net) to recognize the license plate numbers and letters. Our method performs well for different vehicle types such as sedans, SUVs, buses, motorbikes, and trucks. The system works reliably on images of the front and rear views of the vehicle, and it also overcomes tilted or distorted license plate images and performs adequately under various illumination conditions, and noisy backgrounds. Several experiments have been carried out on various types of images from privately collected and publicly available datasets including OPEN-ALPR (BR, EU, US) which consists of 115 Brazilian, 108 European, and 222 North American images, CENPARMI includes 440 from Chinese, US, and different provinces of Canada and UFPR-ALPR includes 4500 Brazilian license plate images; images of those datasets have several challenges: i.e. single to multiple vehicles in an image, license plates of different countries, vehicles at different distances, and images taken by several types of cameras including cellphone cameras. Our experimental results show that the proposed system achieves 98.04% accuracy on average for OPEN-ALPR dataset, 88.5% for the more challenging CENPARMI dataset and 97.42% for UFPR-ALPR dataset respectively, outperforming the state-of-the-art commercial and academics

Study of object detection and reading(license plate detection and reading)

Object detection means finding the location of the object and recognizing what it is. The techniques used for the object detection are feature matching algorithm, pattern comparison and boundary detection. The feature matching algorithm is used to find the best matching object in the knowledge base and to implement the reconstruction of the object recognized. Our object detection is to detect the license plate detection of the car. To detect the license plate of a car first pre-process the image. The commonly license plate locating algorithms include line detection method, neural networks method, fuzzy logic vehicle license plate locating method. “Connected component analysis” is very easy technique than these techniques. In the pretreatment process we first crop the image. After this we convert the color image to gray level image. After converting into gray level that image is filtered using three different types of filters. They are Average, Median, Weiner filters. After deciding the good filter we will apply the segmentation process using edge detection. After finding the edges we will give the numbers to each connected component and store all the connected components in a matrix called labeling matrix. Extract the required connected component using the labeling matrix and store that in an image. Compare this template with our database using template matching technique. Template matching technique uses the correlation procedure. We will find the correlation coefficient between the two templates. Depending upon the correlation coefficient we will find that how much the two templates are similar to each other

Advances in Character Recognition

This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject

Artificial neural network and its applications in quality process control, document recognition and biomedical imaging

In computer-vision based system a digital image obtained by a digital camera would usually have 24-bit color image. The analysis of an image with that many levels might require complicated image processing techniques and higher computational costs. But in real-time application, where a part has to be inspected within a few milliseconds, either we have to reduce the image to a more manageable number of gray levels, usually two levels (binary image), and at the same time retain all necessary features of the original image or develop a complicated technique. A binary image can be obtained by thresholding the original image into two levels. Therefore, thresholding of a given image into binary image is a necessary step for most image analysis and recognition techniques. In this thesis, we have studied the effectiveness of using artificial neural network (ANN) in pharmaceutical, document recognition and biomedical imaging applications for image thresholding and classification purposes. Finally, we have developed edge-based, ANN-based and region-growing based image thresholding techniques to extract low contrast objects of interest and classify them into respective classes in those applications. Real-time quality inspection of gelatin capsules in pharmaceutical applications is an important issue from the point of view of industry\u27s productivity and competitiveness. Computer vision-based automatic quality inspection and controller system is one of the solutions to this problem. Machine vision systems provide quality control and real-time feedback for industrial processes, overcoming physical limitations and subjective judgment of humans. In this thesis, we have developed an image processing system using edge-based image thresholding techniques for quality inspection that satisfy the industrial requirements in pharmaceutical applications to pass the accepted and rejected capsules. In document recognition application, success of OCR mostly depends on the quality of the thresholded image. Non-uniform illumination, low contrast and complex background make it challenging in this application. In this thesis, optimal parameters for ANN-based local thresholding approach for gray scale composite document image with non-uniform background is proposed. An exhaustive search was conducted to select the optimal features and found that pixel value, mean and entropy are the most significant features at window size 3x3 in this application. For other applications, it might be different, but the procedure to find the optimal parameters is same. The average recognition rate 99.25% shows that the proposed 3 features at window size 3x3 are optimal in terms of recognition rate and PSNR compare to the ANN-based thresholding technique with different parameters presented in the literature. In biomedical imaging application, breast cancer continues to be a public health problem. In this thesis we presented a computer aided diagnosis (CAD) system for mass detection and classification in digitized mammograms, which performs mass detection on regions of interest (ROI) followed by the benign-malignant classification on detected masses. Three layers ANN with seven features is proposed for classifying the marked regions into benign and malignant and 90.91% sensitivity and 83.87% specificity is achieved that is very much promising compare to the radiologist\u27s sensitivity 75%