License Plate Recognition for Stolen Vehicles Using Optical Character Recognition

Author(s):  
Armand Christopher Luna ◽  
Christian Trajano ◽  
John Paul So ◽  
Nicole John Pascua ◽  
Abraham Magpantay ◽  
...  

In today’s world managing the records of attendance of staffs, students, employee or bus is a tedious task. This project focuses on automating the bus attendance process through vehicle license plate recognition. As, the license plate is a feature that is peculiar to every vehicle, it would help in efficiently marking the bus attendance. The bus attendance system using RFID is a time consuming process. Hence we developed a project to efficiently mark attendance using number plate recognition and OCR. The system was trained using faster RCNN model with bus image dataset. The proposed system is the number plate is captured through surveillance camera and the captured image will be passed as an input to the neural network for training and the number plate will be detected. Character extraction is done using OCR and extracted character matched will be checked with the database and the attendance for particular bus will be marked.


Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 3015 ◽  
Author(s):  
Farman Ullah ◽  
Hafeez Anwar ◽  
Iram Shahzadi ◽  
Ata Ur Rehman ◽  
Shizra Mehmood ◽  
...  

The paper proposes a sensors platform to control a barrier that is installed for vehicles entrance. This platform is automatized by image-based license plate recognition of the vehicle. However, in situations where standardized license plates are not used, such image-based recognition becomes non-trivial and challenging due to the variations in license plate background, fonts and deformations. The proposed method first detects the approaching vehicle via ultrasonic sensors and, at the same time, captures its image via a camera installed along with the barrier. From this image, the license plate is automatically extracted and further processed to segment the license plate characters. Finally, these characters are recognized with the help of a standard optical character recognition (OCR) pipeline. The evaluation of the proposed system shows an accuracy of 98% for license plates extraction, 96% for character segmentation and 93% for character recognition.


License plate recognition system plays very important role in various security aspects which includes entry monitoring of a particular vehicle in commercial complex, traffic monitoring , identification of threats and many more. In past few years many different methods has been adopted for license plate recognition system but still there is little more chance to work on real time difficulties which come across while license plate recognition like speed of vehicle, angle of license plate in picture, background of picture or color contrast of image, reflection on the license plate and so on. The combination of object detection, image processing, and pattern recognition are used to fulfill this application. In the proposed architecture , system will capture a small video and using Google's OCR(Optical Character Recognition) system will recognize license number, if that number get found in database gate will get open with the help of Arduino Uno.


This paper discusses about License plate recognition using digital processing of images, where the image of a vehicle is taken and the number plate is then recognized by various layers of digital image processing. The number plate is then allowed to undergo optical character recognition (OCR), this extracts the data and then compares it with a database containing the details of the vehicle. This allows the user to identify the type of vehicle and the identity of the person who is driving the vehicle. It will denote the user about the registration of the vehicle by comparing it with the database of the registered vehicle in the area. The device will consist of a camera which will take the real time footage of the vehicles and a snap from the video of the vehicle is used to recognize the number plate. The processor will process the images and will display the number of the vehicle and the owner of the vehicle in the display, this is achieved by comparing the number of the vehicle with the previously fed data from the database. This device will provide an efficient way for automating a parking system where there will be no need for a human to interfere with the checking of the vehicle and providing passes for the vehicle.


The vehicles playing the vital role in our day to day life for transport, and some of the vehicles violates the traffic rules are also increasing, vehicle theft, unnecessary entering into highly restricted areas, increased number of accidents lead to increase in the rate of crime slowly. The vehicle had its own identity it should be recognized which plays the major role in the world. For recognition of the vehicles which are used commonly in the field of safety and security system, LPDR plays a major role and the vehicle registration number is recognized at some certain distance accurately. License Plate recognition is the most efficient and cost effective technique used for detection and recognition purposes. Automatic license plate recognition (ALPR) is used for finding the location of the license plate in the vehicle. These methods and techniques vary based on the conditions like, quality of the image, vehicle on a fine-tuned position, effects of lighting, type of image, etc. The objective is to design an efficient automatic conveyance identification system of sanctioned or unauthorized in the residential societies by utilizing the conveyance number plate. By getting the car image from the surveillance camera in the entrance, we recognizing the number plate and the characters are extracted using OCR (optical character recognition). It converts the character in the image to plain text. Then the plain text of the license plate is cross-verified with the database to check whether the vehicle belongs to residents or visitor. It sends the alert message to the security official when a new visitor request method in a residential area. The log details are stored separately for the resident and visitor in the database. It also provides the details about the parking area availability in the residential area. By calculating the number of vehicles in and out of the area, the detail or availability parking slot is displayed and it sis robust to the size, lighting effects with high rate of detection.


Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 55
Author(s):  
Nicole do Vale Dalarmelina ◽  
Marcio Andrey Teixeira ◽  
Rodolfo I. Meneguette

Automatic License Plate Recognition has been a recurrent research topic due to the increasing number of cameras available in cities, where most of them, if not all, are connected to the Internet. The video traffic generated by the cameras can be analyzed to provide useful insights for the transportation segment. This paper presents the development of an intelligent vehicle identification system based on optical character recognition (OCR) method to be used on intelligent transportation systems. The proposed system makes use of an intelligent parking system named Smart Parking Service (SPANS), which is used to manage public or private spaces. Using computer vision techniques, the SPANS system is used to detect if the parking slots are available or not. The proposed system makes use of SPANS framework to capture images of the parking spaces and identifies the license plate number of the vehicles that are moving around the parking as well as parked in the parking slots. The recognition of the license plate is made in real-time, and the performance of the proposed system is evaluated in real-time.


Author(s):  
Michael Plotnikov ◽  
Paul W. Shuldiner

The ability of an automated license plate reading (ALPR) system to convert video images of license plates into computer records depends on many factors. Of these, two are readily controlled by the operator: the quality of the video images captured in the field and the internal settings of the ALPR used to transcribe these images. A third factor, the light conditions under which the license plate images are acquired, is less easily managed, especially when camcorders are used in the field under ambient light conditions. A set of experiments was conducted to test the effects of ambient light conditions, video camcorder adjustments, and internal ALPR settings on the percent of correct reads attained by a specific type of ALPR, one whose optical character recognition process is based on template matching. Images of rear license plates were collected under four ambient light conditions: overcast with no shadows, and full sunlight with the sun in front of the camcorder, behind the camcorder, and orthogonal to the line of sight. Three camcorder exposure settings were tested. Two of the settings made use of the camcorder’s internal light meter, and the third relied solely on operator judgment. The license plates read ranged from 41% to 72%, depending most strongly on ambient light conditions. In all cases, careful adjustment of the ALPR led to significantly improved read rates over those obtained by using the manufacturer’s recommended default settings. Exposure settings based on the operator’s judgment worked best in all instances.


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