Vehicle Number Plate Recognition System Research Articles

Vehicle Number Plate Recognition

Recognizing vehicle number plates is a difficult but much needed system. This is very useful for automating toll booths, automated signal breakers identification, finding out traffic rule breakers and to detect stolen vehicles. The developed system consist of a Raspberry Pi based vehicle number plate recognition system that automatically recognizes vehicle’s number plate using image processing. The system uses a digital camera interfaced to a Raspberry pi processor. A rear image of vehicle is captured and processed using various algorithms. The system constantly processes incoming camera footage to detect any trace of number plates. On sensing a number plate in front of the camera, it processes the camera input and extracts the number plate part from the image. The image is extracted using OCR. And by using CNN techniques, the number is extracted from the image. The system then displays the extracted number. The implemented system consist of a fully functional vehicle number plate recognition system using Raspberry Pi considering success rate and processing time as parameters. The developed system can be used for security purpose in housing societies to monitor the entry/exit of authorized vehicles. It is observed that the developed system successfully detects and recognizes the vehicle number plate on real time images. The system accuracy is about 80%

The Intelligent Vehicle Number Plate Recognition System based on Arduino

Abstract: The thesis proposes the development of The Intelligent Vehicle Number Plate Recognition System (IVNPRS) using the Arduino platform. The system uses image processing techniques to automatically detect and recognize vehicle number plates, and it includes a camera module, an image processing unit, and an Arduino microcontroller. The proposed system has practical applications such as security monitoring, automatic toll collection, and traffic monitoring. The thesis discusses the different components of the system, image processing techniques used, and evaluates its performance by measuring accuracy, speed, and robustness under different lighting and weather conditions. The experimental results suggest that the VNPRS has the potential to improve vehicular traffic and safety in various locations.

Automatic vehicle number plate recognition system

In simple terms, Automatic Vehicle Number Plate Recognition System (AVNPRS) can be described as a computer vision image processing technology. This technology captures images of vehicles and recognizes their number plates. As the world is becoming more sophisticated on each new day, security as well as management of transportation system have become a vital aspect in controlled places. Such places include modern campuses, shopping malls and other institutions. With today’s rate at which motor vehicles are increasing in numbers, there is need for an effective, affordable and efficient AVNPRS. Thus, this project proposes, outlines and discusses an efficient AVNPRS. The system under consideration in this project can be installed at main entrances of modern institutions. This is because the AVNPRS ensures that only authorized vehicles can automatically have access to such institutions. The AVNPRS ensures that it captures the image of the number plate once the input sensor detects the vehicle. The Sobel edge detection and Laplacian edge detection techniques are used at this critical stage. Thereafter, the Bounding box technique is used to find the number plate leading to character segmentation. After capturing, an image undergoes extraction and character investigation via the Optical Character Recognition (OCR). In addition, in achieving character recognition, matching between the computer template and segmented image is done via the OCR method. It is vital to mention that this system is sustainable as it successfully detects, recognizes and processes vehicle number plates on real images. The AVNPRS can be used for both traffic control and security. Therefore, the main aim of this piece of writing is to develop from a theoretical perspective an AVNPR system that can detect and capture vehicle number plate images.

Automatic Number Plate Recognition System (ANPR)

Object detection and Image processing are combinedly vast area of research. Various Notable works have been done in the field of transportation in order to ensure a healthy and lawful traffic environment. A precise and quick vehicle number plate recognition system is a solution to traffic control and resultant in improved regulations. This paper is based on optical character recognition which uses improved techniques and ensures optimized results. A full fledged algorithm of number plate recognition is proposed and the same acquires upgraded accuracy over existing system. The entire system can be mounted as a solution traffics issues related to law and security. Automatic Number Plate Recognition (ANPR) system consists integration of machine learning together with image processing and pattern identification. Optical character recognition (OCR) is an algorithm that plays an important role in automatic number plate recognition and it is one of the main aspect of Research in machine learning and computer vision that has evolved immensely since its inception.

Automatic Vehicle Number Plate Recognition Approach Using Color Detection Technique

An Automatic Vehicle Number Plate Recognition System (AVNPR) is a key research area in image processing. Various techniques are developed and tested by researchers to improve the detection and recognition rate of AVNPR system but faced problems due to issues such as variation in format, lighting conditions, scales, and colors of number plates in different countries or states or even provinces of a country. Douglas Peucker Algorithm for shape approximation has been used in this research to detect the rectangular contours and the most prominent rectangular contour is extracted as a number plate (NP) and the connected component analysis is used to segment the characters followed by optical character recognition (OCR) to recognize the number plate characters. A custom dataset of 210 vehicle images with different colors at various distances and lighting conditions was used for the proposed method captured on my smart phone Galaxy J7 Model SM-j700F at roads and parking. The dataset contains various types of vehicles (i.e. Trucks, motorcars, mini-buses, tractors, pick-ups etc). The proposed method shows an average result of 95.5%. The novelty used in this method is that it works for different colors simultaneously because in Pakistan, several colors are used for vehicle NPs.

A study of the automated vehicle number plate recognition system

Automatic Number Plate Recognition (ANPR) uses number plates to identify vehicles. The goal of an automated vehicle identification system is to identify the vehicle based on the number plate. The system enforces the regulations, parking, etc. It can also be used at the entrance to protect a large area, such as a military zone or the region around important government buildings like the military base, Parliament, Supreme Court, etc. The smart technology recognizes and captures the image of the vehicle. The number plate area of the vehicle is extracted using image segmentation on the image. Optical character recognition is used for character recognition. The performance data may also be compared to database records to determine the car owner, enrollment location, residence, etc. The testing showed that the improved algorithm easily recognized the number plate of a vehicle on genuine photographs.

Vehicle Number Plate Recognition System to Identify the Authenticated Owner of Vehicles

Vehicle Number Plate Recognition System to Identify the Authenticated Owner of Vehicles

Performance Analysis of Vehicle Number Plate Recognition System Using Template Matching Techniques

Vehicle number plate recognition (VNPR) system is a digital image processing techniques which is broadly used in vehicle transportation system to identify the vehicle by their number plate. Yet it’s a very challenging problem, due to the diversity of plate formats, different scales, and non-uniform illumination conditions during image acquisition. This research mainly focuses on Nepali vehicle number plate recognition system in which the vehicle plate image is received by the digital cameras and the image was then processed to obtain the number plate information. A real image of a vehicle is captured and processed using various algorithms. Morphological operations, and edge detection, smoothing, filtering, techniques for plate localization and characters segmentation for segment character and these segmented character was cut in to block of 70×70 size and calculate the correlation with the template of database using the template matching algorithm normalized cross correlation and phase correlation and compare this result in term of accuracy. The system was tested by 90 patterns under several conditions. It includes experiment of number plate recognition using phase correlation and normalized cross correlation methods. From the study and analysis of test after applying on number of images of database, the normalized cross correlation method was found more accurate to recognize the number plate then phase correlation method and recognition accuracy of normalized cross correlation was 67.98% and phase correlation was 63.46%.

Video Analytic Algorithm for License Plate Recognition System

Security has always been the most dominant factor in all domains of everyday life. Companies are spending more and more in this domain as security have become an international issue especially after war on terror era. Not only national departments have become victim of it but general public has also suffered a huge deal due to the lack of security measures in the recent past. This is one of the main reasonfor investment in this domain. From traffic monitoring and security purposes, a vehicle number plate recognition system can play a very useful role in monitoring the vehicle's movement and consequently providing information about the vehicle. Different countries have their own system of issuing number plates. Similarly different algorithms are designed for number plate recognition in different countries. The proposed system constitutes an algorithm which is designed for the vehicles residing in Pakistan. Digital image processing techniques are the basis of the proposed system including the image enhancement and filtering techniques for noise and other weather effects reduction. Hough transform is used to segment the characters and consequently recognize the character. A very large data set has been used to test the system which shows quite immaculate results.

An associative processing module for a heterogeneous vision architecture

The heterogeneous vision architecture that satisfies the computing demands of real-time computer vision by providing parallelism in three different forms is described. A pipeline of digital signal processing (DSP) chips initially processes signals. Then a SIMD associative processor array processes images and extract features, and a MIMD network of transputers processes extracted objects in parallel. The array's VLSI implementation, the processing modes available due to the use of content-addressable memory, and the means of achieving efficient 2-D interprocessor communication in the linear array are described. An application as a vehicle number plate recognition system is presented. >