A Novel Method for Indian Vehicle Registration Number Plate Detection and Recognition using Image Processing Techniques

Traffic law violation has been recognized as a major cause for road accidents in most parts of the world with majority occurring in developing countries. Even with the presence of rules and regulations stipulated against this, violators are still on the increase. This is due to the fact that the rules are not properly enforced by appropriate authorities in those parts of the world. Therefore, a system needs to be designed to assist law enforcement agencies to impose these rules to improve road safety and reduce road accidents. This work uses a Vehicle Plate Number Recognition (VNPR) system which is a real-time embedded system to automatically recognize license plate numbers. It provides an alternative means to VPNR using an open-source library known as openCV. The main aim of the system is to use image processing to identify vehicles violating traffic by their plate numbers. It consists of an IR sensor for detecting the vehicle. During testing, a minimum time was set for the sensor to detect the object which was recorded by the microprocessor. Once it was less than the set time, the camera was triggered to capture the plate number and store the image on the Raspberry Pi. The image captured is processed by the Raspberry Pi to extract the numbers on the image. The numbers on the capture imaged were viewed on a web page via an IP address. The system if implemented can be used to improve road safety and control traffic of emerging smart cities. It will also be used to apply appropriate sanctions for traffic law violators.

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Development of Smart Plate Number Recognition System for Fast Cars with Web Application

Recognition of Vehicle License Number Plates (VLNP) is an important task. It is valuable in numerous applications, such as entrance admission, security, parking control, road traffic control, and speed control. An ANPR (Automatic Number Plate Recognition) is a system in which the image of the vehicle is captured through high definition cameras. The image is then used to detect vehicles of any type (car, van, bus, truck, and bike, etc.), its’ color (white, black, blue, etc.), and its’ model (Toyota Corolla, Honda Civic etc.). Furthermore, this image is processed using segmentation and OCR techniques to get the vehicle registration number in form of characters. Once the required information is extracted from VLNP, this information is sent to the control center for further processing.

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An Efficient Approach for Vehicle Number Plate Recognition in Pakistan

Transportation system (vehicle communication) plays a major role in today’s scenario. Detection of vehicle number plate exactly in blurry conditions was the most challenging issue found in the last three decades. Although many intensive studies were undertaken, none addressed this problem exhaustively. Various methods are introduced by several researchers for detecting the vehicle number from the vehicle number plate images. The purpose of this study was to investigate this current issue by implementing an edge-based approach on the basis of quantitative combination of Canny, Morphological and Sobel methods for the accurate detection of vehicle number in blurry conditions. The experimental results demonstrated that the proposed scheme outperforms its counterparts in terms of Sobel, Prewitt, Roberts, Laplacian of Gaussian (LoG), Morphological and Canny methods in all aspects with higher peak signal to noise ratio (PSNR) and signal to noise ratio (SNR) values. Hence, the proposed hybrid scheme is better and robust and results in accurate estimation of vehicle number from the blurry vehicle number plate (BVNP) images for the given datasets.

Vehicle Number Plate Detection: An Edge Image Based Approach

Many road accidents occur due to drivers failing to read sign board due to various reasons. Especially at night, the tiredness of driver reduces his perception to small things like speed limit of sign the board, curve ahead sign board. For the smooth movement of ambulance in cities during traffic, is to create an IOT device to detect sign boards and also able to com-municate with the traffic light and makes way for ambulance. Implementation is done by detecting sign boards and measuring speed of vehicle using arduino and RF transmitter which transmits the specific beep sound to specific type of application like speed breaker, school zone etc. The vehicle also contains RF receiver and arduino, which starts receiving the beep sound when near to sign board. After receiving the code, arduino starts measuring the current speed of vehicle and if the speed is above recommended speed then it starts gives alert. If the vehicle speed is not reduced even after the alert then the vehicle will auto break. With the help of this Traffic Management System (TMS), we can record the number of users who do not reduce vehicle speed even when prompted by the system alerts.

Traffic control Management System and Collision Avoidance System.

Vehicles play a vital role in modern-day transportation systems. Number plate provides a standard means of identification for any vehicle. To serve this purpose, automatic license plate recognition system was developed. This consisted of four major steps: preprocessing of obtained image, extraction of license plate region, segmentation, and character recognition. In earlier research, direct application of Sobel edge detection algorithm or applying threshold was used as key steps to extract the license plate region, which do not produce efficient results when captured image is subjected to high intensity of light. The use of morphological operations causes deformity in the characters during segmentation. We propose a novel algorithm to tackle the mentioned issues through a unique edge detection algorithm. It is also a tedious task to create and update the database of required vehicles frequently. This problem is solved by the use of ‘Internet of things’ where an online database can be created and updated from any module instantly. Also, through IoT, we connect all the cameras in a geographical area to one server to create a ‘universal eye’ which drastically increases the probability of tracing a vehicle over having manual database attached to each camera for identification purpose.

Efficient License Plate Recognition System with Smarter Interpretation Through IoT

In today's world road and transport has become an integral part of every human being. The present transport system has minimized the distances but it has on the other hand increased the life peril. The focal idea of this paper is very functional to implement as road safety measure. This paper proposes a novel system where it is able to automatically control the speed of the vehicle by detecting the speed signs labels from speed sign boards, which are laid on the road side and take necessary steps to take it to the knowledge of the driver by sending a caution notification. When the driver does not speed down even after the sign of caution, details of the vehicle are notified to the authorities (traffic officials) and the speed of the car is reduced to threshold speed limit as on the speed label. This paper proposes an innovative approach by using two wide scoped fields and relevant knowledge to its fullest. a real time image processing software MATLAB identifies speed sign label, ARDUINO UNO to send that data to cloud and GPRS module to send location of the vehicle. The sent data gets analysed by PHP in the cloud and necessary steps are executed according to the results of the analysis.

K. Ashok Reddy

Papers

Efficient License Plate Recognition System with Smarter Interpretation Through IoT

Vehicle Speed Control and Over-Speed Violation Alert Using IoT and Hall Effect Sensor