Automatic vehicle license plate recognition

License Plate Recognition (LPR) plays an important role on the traffic monitoring and parking management. A robust and efficient method for enhancing accuracy of license plate characters recognition based on K Nearest Neighbours (K-NN) classifier is presented in this paper. The system first prepares a contour form of the extracted character, then the angle and distance feature information about the character is extracted and finally K-NN classifier is used to character recognition. Angle and distance features of a character have been computed based on distribution of points on the bitmap image of character. In K-NN method, the Euclidean distance between testing point and reference points is calculated in order to find the k-nearest neighbours. We evaluated our method on the available dataset that contain 1200 sample. Using 70% samples for training, we tested our method on whole samples and obtained 99% correct recognition rate.Further, we achieved average 99.41% accuracy using three...

— In this paper, a new algorithm for vehicle license plate identification is proposed, on the basis of a novel adaptive image segmentation technique (Sliding Concentric Windows-SCW) and connected component analysis in conjunction with a character recognition Neural Network. The algorithm was tested with 1334 natural scene gray level vehicle images of different backgrounds and ambient illumination. The camera focused in the plate, while the angle of view and the distance from the vehicle varied according to the experimental setup. The license plates properly segmented were 1287 over 1334 input images (96.5%). The Optical Character Recognition (OCR) system is a two layer Probabilistic Neural Network (PNN) with topology 108-180-36, whose performance for entire plate recognition reached 89.1%. The PNN is trained to identify alphanumeric characters from car license plates based on data obtained from algorithmic image processing. Combining the above two rates, the overall rate of success for our LPR algorithm is 86.0%. A review in the related literature presented in this paper, reveals that better performance (90% up to 95%) has been reported, when limitations in distance, angle of view, illumination conditions are set and background complexity is low.

The growing Tanzanian population currently estimated to be 48 Million people and their use of vehicles as means of transport has kept increasing making enforcing traffic rules and regulations among road users a major challenge. This calls for a need to have an automated system that monitors the motorists with a pre-defined sense of intelligence. A Vehicle Detection and Recognition Algorithm which can provide automated access to relevant information to a number plate from information systems containing and managing databases on vehicle and their movements is required. This paper presents work on developed algorithm that localizes plate area, extract and segment character, and finally recognizes and interprets registration number from vehicle image. MATLAB R2012b Simulation software with Image Processing toolbox is employed. HSV color space image, morphological and statistical analysis operations were integrated and employed to a vehicle image to compute plate number area. In segmentation the properties like aspect ratio, extent, and area ratio were important measurement parameters. Finally, the template matching database and statistical character extracted from car image was correlated to recognize alphanumeric character to deduce car registration number.

 Abstract: In current situation, we come across various problems in traffic regulations in India which can be solved with different ideas. Riding motorcycle/mopeds without wearing helmet is a traffic violation which has resulted in increase in number of accidents and deaths in India. Existing system monitors the traffic violations primarily through CCTV recordings, where the traffic police have to look into the frame where the traffic violation is happening, zoom into the license plate in case rider is not wearing helmet. But this requires lot of manpower and time as the traffic violations frequently and the number of people using motorcycles is increasing day-by-day. What if there is a system, which would automatically look for traffic violation of not wearing helmet while riding motorcycle/moped and if so, would automatically extract the vehicles' license plate number. Recent research have successfully done this work based on CNN, R-CNN, LBP, HoG, HaaR features,etc. But these works are limited with respect to efficiency, accuracy or the speed with which object detection and classification is done. In this research work, a Non-Helmet Rider detection system is built which attempts to satisfy the automation of detecting the traffic violation of not wearing helmet and extracting the vehicles' license plate number. The main principle involved is Object Detection using Deep Learning at three levels. The objects detected are person, motorcycle/moped at first level using YOLOv2, helmet at second level using YOLOv3, License plate at the last level using YOLOv2. Then the license plate registration number is extracted using OCR (Optical Character Recognition). All these techniques are subjected to predefined conditions and constraints, especially the license plate number extraction part. Since, this work takes video as its input, the speed of execution is crucial. We have used above said methodologies to build a holistic system for both helmet detection and license plate number extraction.

Use of automated licence/number plate recognition (‘ALPR/ANPR’) technologies in Canada and Australia raises significant policy questions for privacy advocates and criminal justice practitioners. The proliferation of mass surveillance through ALPR/ANPR also presents several conceptual puzzles about the links among criminal justice data flows, individual privacy and state responsibility in this actuarial age. In this article, we use case studies of ALPR/ANPR in Canada and Australia to examine privacy as a technique for governing road traffic surveillance. We explain our findings in light of Harcourt’s (2007) argument against the use of actuarial prediction and ‘hit rates’ that are rationalised as the chief measure of law enforcement activities and effectiveness. Finally, we question the regulation of surveillance technologies such as ALPR/ANPR through current Canadian and Australian information privacy laws, with specific focus on privacy by design (‘PbD’), a strategy that favours improving law enforcement efficiency at the expense of privacy.

Internet of Things (IoT) plays an important role in connecting the surrounding things of an environment to the network and provides easy access from the remote location. It is essential that the people should be updated along with the growing technology. In the present world, there is a numerous number of vehicles owned by the people. Due to this large number of vehicles, there will be a lot of discomfort for parking of a vehicle at public places such as shopping complexes, function halls, and cinema theatres. In such places, it is very difficult to find a vacant place to park the vehicle, even though it is a paid facility along with the security guard. In this study, we design an Advanced Smart Parking System (ASPS) which identifies the vehicle and displays an empty slot on the display at the entrance gate of the parking. So that the person will know whether there is any availability or not before entering into the parking place. This system of design also provides the shortest path from the entrance gate to the assigned parking slot by showing the directions through leds. This process of implementation does not involve human interaction except the payment collection.

This paper deals with a novel vehicle manufacturer and model recognition scheme, which is enhanced by color recognition for more robust results. A probabilistic neural network is assessed as a classifier and it is demonstrated that relatively simple image processing measurements can be used to obtain high performance vehicle authentication. The proposed system is assisted by a previously developed license plate recognition, a symmetry axis detector and an image phase congruency calculation modules. The reported results indicate a high recognition rate and a fast processing time, making the system suitable for real-time applications.

License plate recognition (LPR) algorithms in
images or videos are generally composed of three processing
steps: extraction of a license plate region, segmentation of the
plate characters and finally recognition of each character. This
task is quite challenging due to the diversity of plate formats and
the non-uniform outdoor illumination conditions during image
acquisition. Therefore, most approaches work only under
restricted conditions, such as fixed illumination, limited vehicle
speed, designated routes and stationary backgrounds. Numerous
techniques have been developed for LPR in still images or video
sequences, and the purpose of this paper is to categorize and
assess them. Issues such as processing time, computational power
and recognition rate are also addressed, when available. Finally,
this paper offers to researchers a link to a public image database
in order to define a common reference point for LPR algorithmic
assessment.

In the today scenario technological intelligence is a higher demand after commodity even in traffic-based systems. These intelligent systems do not only help in traffic monitoring but also in commuter safety, law enforcement and commercial applications. The proposed Saudi Arabia Vehicle License plate recognition system splits into three major parts, firstly extraction of a license plate region secondly segmentation of the plate characters and lastly recognition of each character. This act is quite challenging due to the multiformity of plate formats and the nonuniform outdoor illumination conditions during image collection. In this paper recognition of the license plates is achieved by the implementation of the Learning Vector Quantization artificial neural network. Their results are based upon their completeness in the Saudi Arabia Vehicle License plate character recognition and theirs have obtained encouraging results from proposed technique.

In the today scenario technological intelligence is a higher demand after commodity even in traffic-based systems. These intelligent systems do not only help in traffic monitoring but also in commuter safety, law enforcement and commercial applications. The proposed Saudi Arabia Vehicle License plate recognition system splits into three major parts, firstly extraction of a license plate region secondly segmentation of the plate characters and lastly recognition of each character. This act is quite challenging due to the multiformity of plate formats and the nonuniform outdoor illumination conditions during image collection. In this paper recognition of the license plates is achieved by the implementation of the Learning Vector Quantization artificial neural network. Their results are based upon their completeness in the Saudi Arabia Vehicle License plate character recognition and theirs have obtained encouraging results from proposed technique.