Security System Based on Automatic License Plate Recognition

In this project, a simple technique is presented for Automated Number Plate Recognition (ANPR) System, which can be used many applications for automated recognition of vehicle number plates & can be applied to garage door security. A simple algorithm(CNN) is designed that can help to recognize number plates of vehicles using images taken by camera. The recognition of number plate’s algorithm has five parts: Acquisition image, pre-processing, Edge detection and segmentation, feature extraction and recognition of character of number plates using suitable ML algorithms. 

INTRODUCTION: Garage doors are traditionally weak links in garage security, giving thieves easy access to our home. For the purpose of Securing the garage we applied Image Processing method to recognize number plate of a car, and when the plate number matches with stored database plate number door get unlocked. Automatic number plate recognition (ANPR)is a mass surveillance method that uses optical character recognition on images to read vehicle registration plates. Massive integration of information technologies into all aspects of modern life caused demand for processing vehicles as conceptual resources in information systems. Because a standalone information system without any data has no sense, there was also a need to transform information about vehicles between the reality and information systems. This can be achieved by a human agent, or by special intelligent equipment which is be able to recognize vehicles by their number plates in a real environment and reflect it into conceptual resources. Because of this, various recognition techniques have been developed

and number plate recognition systems are today used in various traffic and security applications, such as parking, access and border control, or tracking of stolen cars.

           In entrance gate, number plates are used to identify the vehicles. When a vehicle enters an input gate, number plate is automatically recognized and stored in database and black-listed number is not given permission. When a vehicle later exits the place through the gate, number plate is recognized again and paired with the first-one stored in the database and it is taken a count. Automatic number plate recognition systems can be used in access control.

For example, this technology is used in many companies to grant access only to vehicles of authorized personnel.

           In some countries, ANPR systems installed on country borders automatically detect and monitor border crossings. Each vehicle can be registered in a central database and compared to a black list of stolen vehicles. In traffic control, vehicles can be directed to different lanes for a better congestion control in busy urban communications during the rush hours.

           Automatic Line Tracking Robot (ALR) is used in this project as a vehicle which contains circuitry for moving in a guided track. It will have mechanism to detect the opened and closed door. It also will have capacity to park in the given parking area.

Past Projects: Although there have been no similar ECE-3200 projects in the past, we have been able to find a few research papers on the topic of license plate recognition systems as well as several commercial license plate recognition systems. We performed microcontroller based projects like line follower robot,wall following robot.

Problem Statement: The main focus in this project is to experiment deeply with, and find solutions to the image segmentation and character recognition problems within the License Plate Recognition framework. Three main stages are identified in such applications. First, it is necessary to locate and extract the license plate region from a larger scene image. Second, having a license plate region to work with, the alphanumeric characters in the plate need to be extracted from the background. Third, deliver them to an OCR system for recognition. In order to identify a vehicle by reading its license plate successfully, it is obviously necessary to locate the plate in the scene image provided by some acquisition system (e.g. video or still camera). Locating the region of interest helps in dramatically reducing both the computational expense and algorithm complexity. For example, a currently common 1024×768 resolution image contains a total of 786,432 pixels, while the region of interest (in this case a license plate) may account for only 10% of the image area. Also, the input to the following segmentation and recognition stages is simplified, resulting in easier algorithm design and shorter computation times. The project mainly work with some English letters of license plates but the techniques, algorithms and parameters that is be used can be adjusted easily for any similar number plates even with other alpha-numeric set of bangla font.

Proposed Solution: The process of automatic number plate recognition consists of four main stages:

 (1) Preprocessing

(2) License plate localization

(3) Character segmentation

(4) Character recognition

The Constraints: We have, a set of constraints have been placed on our system to make the project more manageable, they are as follows:

• Image of the vehicle taken from fixed angle.
• Image of the vehicle taken from fixed distance.
• Vehicle is stationary when the image was taken.
• Only English letters license plates will be dealt with.

System Overview: Description of the method: The images of vehicles were taken with a Sony digital camera, borrowed from the MEMS lab, with a resolution of 480×640.  On average, the images were taken seven feet away from the vehicle.  They were stored in color JPEG format on the camera.  We use Matlab to convert the color JPEG images into gray scale raw format on the PC.  An interface on the PC side then transfers the images to the EVM for processing.

Image Processing Overview:

Block Diagram

General overview of Door security with ALPR

Block Diagram

Processing the image of the car

Character recognition: There are six primary algorithms that the software requires for identifying a licence plate:

1.Plate localisation – responsible for finding and isolating the plate on the picture

2.Plate orientation and sizing – compensates for the skew of the plate and adjusts the dimensions to the required size

3.Normalisation – adjusts the brightness and contrast of the image

4.Character segmentation – finds the individual characters on the plates

5.Optical character recognition

6.Syntactical/Geometrical analysis – check characters and positions against country specific rules

1. Poor image resolution, usually because the plate is too far away but sometimes resulting from the use of a low-quality camera.
2. Blurry images, particularly motion blur and most likely on mobile units
3. Poor lighting and low contrast due to overexposure, reflection or shadows
4. An object obscuring the plate, quite often a tow bar, or dirt on the plate
5. A different font, popular for vanity plates
6. Circumvention techniques

Basic Functions in ANPR:

Applications:

• It can walk like a human
• It is a personal assistance robot to help the user
• It can sense smoke in dirty or dangerous jobs like as coal mine.
• It can dance to entertain the user.
• It alarmed human in dangerous region.

Future Work:

• Adding Bangla font to recognize Bangladeshi Number Plate
• Implement Face recognition
• Increase the accuracy of Plate detecting.

A photo of our project

Conclusion: We applied Automated Number Plate Recognition (ANPR) System for door security successfully. We have some limitations at time of  giving legal authority of the number plate font. Our future target is to add barcode security and bangla font & to serve the Bangladeshi people to make their life more secure.