REAL TIME NUMBER PLATE RECOGNITION SYSTEM

NUMBER PLATE RECOGNITION SYSTEM


Real Time Number Plate Recognition System is an image processing technology which uses number (license) plate to identify the vehicle. The objective is to design an efficient automatic authorized vehicle identification system by using the vehicle number plate. Number plate recognition (NPR) can be used in various fields such as vehicle tracking, traffic monitoring, automatic payment of tolls on highways or bridges, surveillance systems, tolls collection points, and parking management systems. The developed system first detects the vehicle and then captures the vehicle image. Vehicle number plate region is localized using Neural Network then image segmentation is done on the image. Character recognition technique is used for the character extraction from the plate. The resulting data is then stored in a database along with the time-stamp. The system is implemented and simulated in python, and its performance is tested on real image.

BACKGROUND

In last few years, Automatic Number Plate Recognition (ANPR) has been one of the useful approaches for vehicle surveillance. It is can be applied at number of public places for fulfilling some of the purposes like traffic safety enforcement, automatic toll text collection , car park system and Automatic vehicle parking system. In automated systems, people utilize computer‐based expert systems to analyze and handle real‐life problems such as intelligent transportation systems. Presently number plate detection and recognition processing time is less than 50 milliseconds in many systems.

Nepali number plate character are selected from the pool of 29 characters in a specific orders. Order defines various characteristic of the number plates such as vehicle type, vehicle load, etc. The number plates used in Nepal are usually of two formats one containing all the characters in a single row and the other containing two rows of characters. Characters are selected from Devanagari script.

Nepali vehicles have license numbers encoded in the both rear and front side with two different sized rectangular plates. The front sized plates are usually in 4: 1 ratio and the back sized plates are in 4: 3 ratio.

PROBLEM STATEMENT

The escalating increase of contemporary urban and national road networks over the last decades emerged the need of efficient monitoring and management of road traffic. Meanwhile, rising vehicle use causes social problems such as accidents, traffic congestion, and consequent traffic pollution.

Real Time Number Plate Recognition is a process where vehicles are identified or recognized using their number plate or license plate. RTNPR uses image processing techniques so as to extract the vehicle number plate from digital images.

RTNPR systems normally comprises of two components: A camera that used in capturing of vehicle number plate images, and software that extracts the number plates from the captured images by using a character recognition tool that allows for pixels to be translated into numerical readable characters. It is used widely in various fields such as vehicle tracking, traffic monitoring, automatic payment of tolls on highways or bridges,

Surveillance systems, tolls collection points, and parking management systems. ANPR algorithms are generally divided in four steps:

(1) Vehicle image acquisition

(2) Number plate extraction

(3) Character segmentation and

(4) Character recognition.

The first step i.e. to capture image of vehicle looks very easy but it is quite exigent task as it is very difficult to capture image of moving vehicle in real time in such a manner that none of the component of vehicle especially the vehicle number plate should be missed. The success of fourth step depends on how second and third step are able to locate vehicle number plate and separate each character.

SCOPE OF THE PROJECT

This project is developed for detecting license plate from the vehicle and to store the extracted characters of the number plate in database along with their timestamp. The scope of this project is to develop real time number plate recognition system which can be implemented in vehicle tracking, traffic monitoring, automatic payment of tolls on highways or bridges, surveillance systems, tolls collection points, and parking management systems.

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Functional Requirements

  1. Capabilities for automatic recognition of vehicles, number plates localization and feature extraction

  2. Store the features extracted from the license plate

  3. Retrieve, modify information if there is a need for evidence in disputes or crime situations

Non-Functional Requirements

  1. Secure access of confidential data (user’s details)

  2. 24 X 7 availability and should be efficient

  3. Better component design to get better performance at peak time.

  4. Flexible service based architecture will be highly desirable for future extension.

  5. The system must display necessary information in case of failure preventing system breakdown.

Hardware Requirements

1. PC/ Laptop/Server

2. Surveillance Camera

Software Requirements

1. Python 3.7 & Open CV

Planning & Requirement Analysis

In this phase requirements for the software development are analyzed and documented. In our case, the system should detect the vehicle, localize the number plate, Segment the character and store the character in database.

Design

Here, architecture of the system is designed which resembles that how system look in front of the user. Using Tkinter we will design the user interface of the system.

Building

After gathering all the requirements and designing the model, the development is proceed. In every development phase small prototype of the system is developed and analyzed by the user. In our system, small prototype is made that simply detects the vehicle. Furthermore, the system will be developed to localize the number plate, segment the number plate and extract the character sequentially.

 

Testing

The developed prototype is checked whether it satisfies the requirement as specified previously in the requirement analysis. Here, time an again, the system is checked whether the detected vehicle is correct or not. Also the system is verified if it gives correct features from the number plate, also if there is any error in the system process.

Final Product

At the end of the iteration, a working product is displayed to the customer and important stakeholders. The system then localize number plate from the moving vehicle and extract the features after segmenting the characters of the number plate.

RESULT AND ANALYSIS

After the completion of project, our system is able to localize the number plate and extract the characters from the plate in real time. The accuracy of plate localization was 92% and the accuracy of predicting the characters from segmented characters was 96%. 

CONCLUSION

We have created a model using convolution Neural Network and Yolo algorithm that detects the vehicle, localize the number plate and extract and predict the characters. Data were collected from various data source and were pre-processed using various image processing techniques. We created our own weights and biases to pass into defined CNN function. CNN’s different layer was used to extract the features of images and predict the character from the extracted number plate.

REFERENCES

  1. Pant and P. Gyawali. “Automatic Nepali Number Plate Recognition with Support Vector Machines”.Tribhuwan University.December 2015

  2. LIU, Yu Han. “Feature Extraction and Image Recognition with Convolutional Neural Networks” .University of Electronic Science and Technology of China. 2018

  3. Redmon J. and Girshik R. “You Only Look Once: Unified, Real-Time Object Detection”.CPVR, 2016.

  4. D. Zheng, Y. Zhao, and J. Wang, “An efficient method of license plate location,” Pattern Recognition Letters, vol. 26, no. 15, pp. 2431–2438,2005.

  5. D. P. Suri, D. E. Walia, and E. A. Verma, “Vehicle number plate detection using sobel edge detection technique,” International Journal of Computer Science and Technology, ISSN, pp. 2229–4333, 2010.

  6. Massoud MA, Sabee M, Gergais M, Bakhit R (2013) Automated NEW LICENSE Plate Recognition in Egypt. Alexandria Engineering Journal 52: 319-326.

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