This report describes the Smart License Plate Reorganization System, which can be installed into a tollbooth for automated acceptation of vehicle license plate details using an image of a vehicle. This Smart License Plate Reorganization system could then be implemented to control the payment of fees, highways, bridges, parking areas or tunnels, etc. This report contains new algorithm for acceptation number plate using Structural operation, Thresholding operation, Edge detection, Bounding box analysis for number plate extraction, character separation using separation and character acceptation using Template method and Feature extraction.
Number Plate Recognition for Indian Vehiclesmonjuri10
This paper presents Automatic Number Plate
extraction, character segmentation and recognition for
Indian vehicles. In India, number plate models are not
followed strictly. Characters on plate are in different
Indian languages, as well as in English. Due to variations
in the representation of number plates, vehicle number
plate extraction, character segmentation and recognition
are crucial. We present the number plate extraction,
character segmentation and recognition work, with english
characters. Number plate extraction is done using Sobel
filter, morphological operations and connected component
analysis. Character segmentation is done by using
connected component and vertical projection analysis.
Character recognition is carried out using Support Vector
machine (SVM). The segmentation accuracy is 80% and
recognition rate is 79.84 %.
A Review Paper on Automatic Number Plate Recognition (ANPR) SystemAM Publications
Automatic Number Plate Recognition system i.e. ANPR system is an image processing technology. In which
we uses number plate of vehicle to recognize the vehicle. The objective is to design an efficient automatic vehicle
identification system by using the vehicle number plate, and to implement it for various applications such as automatic toll
tax collection, parking system, Border crossings, Traffic control, stolen cars etc. The system has color image inputs of a
vehicle and the output has the registration number of that vehicle. The system first senses the vehicle and then gets an
image of vehicle from the front or back view of the vehicle. The system has four main steps to get the required
information. These are image acquisition, plate localization, character segmentation and character recognition. This
system is implemented and simulated in Matlab 2010a.
Number plate recognition using ocr techniqueeSAT Journals
Abstract Automatic Number Plate Recognition (ANPR) is a special form of Optical Character Recognition (OCR). ANPR is an image processing technology which identifies the vehicle from its number plate automatically by digital pictures. In this paper we have presented an algorithm for vehicle number identification based on Optical Character Recognition (OCR). OCR is used to recognize an optically processed printed character number plate which is based on template matching. This algorithm is tested on different ambient illumination vehicle images. OCR is the last stage in vehicle number plate recognition. In recognition stage the characters on the number plate are converted into texts. The characters are then recognized using the template matching algorithm. Index Terms: Automatic Number Plate Recognition (ANPR), Optical Character Recognition (OCR), Template Matching
Number Plate Recognition for Indian Vehiclesmonjuri10
This paper presents Automatic Number Plate
extraction, character segmentation and recognition for
Indian vehicles. In India, number plate models are not
followed strictly. Characters on plate are in different
Indian languages, as well as in English. Due to variations
in the representation of number plates, vehicle number
plate extraction, character segmentation and recognition
are crucial. We present the number plate extraction,
character segmentation and recognition work, with english
characters. Number plate extraction is done using Sobel
filter, morphological operations and connected component
analysis. Character segmentation is done by using
connected component and vertical projection analysis.
Character recognition is carried out using Support Vector
machine (SVM). The segmentation accuracy is 80% and
recognition rate is 79.84 %.
A Review Paper on Automatic Number Plate Recognition (ANPR) SystemAM Publications
Automatic Number Plate Recognition system i.e. ANPR system is an image processing technology. In which
we uses number plate of vehicle to recognize the vehicle. The objective is to design an efficient automatic vehicle
identification system by using the vehicle number plate, and to implement it for various applications such as automatic toll
tax collection, parking system, Border crossings, Traffic control, stolen cars etc. The system has color image inputs of a
vehicle and the output has the registration number of that vehicle. The system first senses the vehicle and then gets an
image of vehicle from the front or back view of the vehicle. The system has four main steps to get the required
information. These are image acquisition, plate localization, character segmentation and character recognition. This
system is implemented and simulated in Matlab 2010a.
Number plate recognition using ocr techniqueeSAT Journals
Abstract Automatic Number Plate Recognition (ANPR) is a special form of Optical Character Recognition (OCR). ANPR is an image processing technology which identifies the vehicle from its number plate automatically by digital pictures. In this paper we have presented an algorithm for vehicle number identification based on Optical Character Recognition (OCR). OCR is used to recognize an optically processed printed character number plate which is based on template matching. This algorithm is tested on different ambient illumination vehicle images. OCR is the last stage in vehicle number plate recognition. In recognition stage the characters on the number plate are converted into texts. The characters are then recognized using the template matching algorithm. Index Terms: Automatic Number Plate Recognition (ANPR), Optical Character Recognition (OCR), Template Matching
Number plate identification perimeter protection system. Control which vehicle access your premise. Assign rules for vehicles trying to enter the premise. Blacklist vehicles. Generate Alert if a blacklisted or unregistered vehicle trying to enter the area. Make efficient use of your security staff
AUTOMATIC NUMBER PLATE RECOGNITION and Violation processing SECURAWorld
A.I and Surveillance for Smart cities
AUTOMATIC NUMBER PLATE RECOGNITION and Violation processing
A robust AI engine deeply trained to accurately detect, recognize a license plate that works with a variety of non standard formats as well.
An Intelligent system to automatically process challans wherever reads are deemed appropriate, reducing significant time and maximizing coverage.
Helmet Violation
Simultaneously identifying person not wearing helmet and it’s vehicle license plate, even if the person is on the back seat.
This AI, using deep learning methods is also able to identify ‘Triple Riding Violations’.
Smart Parking and Smart Anti-Hawking
Hawking Detection analytics is capable of detecting an illegally parked Hawking station out of the designated area. Resulting in much safer traffic.
Using neural networks + network of cameras to solve a simple yet complex parking problem across your whole campus, while also extracting various vehicle features (make, model, color, etc)
introduction to licence plate recognition technique, optical character recognition, functions used in the program, pros and cons, applications, future scope.
AUTOMATIC LICENSE PLATE RECOGNITION SYSTEM FOR INDIAN VEHICLE IDENTIFICATION ...Kuntal Bhowmick
Automatic License Plate Recognition (ANPR) is a practical application of image processing which uses number (license) plate is used to identify the vehicle. The aim is to design an efficient automatic vehicle identification system by using the
vehicle license plate. The system is implemented on the entrance for security control of a highly restricted area like
military zones or area around top government offices e.g.Parliament, Supreme Court etc.
It is worth mentioning that there is a scarcity in researches that introduce an automatic number plate recognition for indian vechicles.In this paper, a new algorithm is presented for Indian vehicle’s number plate recognition system. The proposed algorithm consists of two major parts: plate region extraction and plate recognition.Vehicle number plate region is extracted using the image segmentation in a vechicle image.Optical character recognition technique is used for the character recognition. And finally the resulting data is used to compare with the records on a database so as to come up with the specific information like the vehicle’s owner, registration state, address, etc.
The performance of the proposed algorithm has been tested on real license plate images of indian vechicles. Based on the experimental results, we noted that our algorithm shows superior performance special in number plate recognition phase.
Automatic license plate recognition system for indian vehicle identification ...Kuntal Bhowmick
Automatic License Plate Recognition (ANPR) is a practical application of image processing which uses number (license) plate is used to identify the vehicle. The aim is to design an efficient automatic vehicle identification system by using the
vehicle license plate. The system is implemented on the entrance for security control of a highly restricted area like
military zones or area around top government offices e.g.Parliament, Supreme Court etc.
It is worth mentioning that there is a scarcity in researches that introduce an automatic number plate recognition for indian vechicles.In this paper, a new algorithm is presented for Indian vehicle’s number plate recognition system. The proposed algorithm consists of two major parts: plate region extraction and plate recognition.Vehicle number plate region is extracted using the image segmentation in a vechicle image.Optical character recognition technique is used for the character recognition. And finally the resulting data is used to compare with the records on a database so as to come up with the specific information like the vehicle’s owner, registration state, address, etc.
The performance of the proposed algorithm has been tested on real license plate images of indian vechicles. Based on the experimental results, we noted that our algorithm shows superior performance special in number plate recognition phase.
Artificial Neural Network (ANN), Automatic Number Plate Recognition (ANPR), Character Segmentation,Edge detection, Extraction plate region, Image Segmentation, Number plate recognition, Number Plate, Optical Character Recognition.
Automatic License Plate Recognition Using Optical Character Recognition Based...IJARIIE JOURNAL
A License plate is a rectangular plate which is alphanumeric. The license plate is fixed on the vehicle and used to
identify the vehicle along with honor of that vehicle. There is a huge number of vehicles on the road so that traffic
control and vehicle owner identification has become a major problem.
The automatic number plate reorganization (ANPR) is one of the solutions of such kind of problem. There are
different methodologies but it is challenging task as some of the factors like high speed of vehicles, languages of
number plate & mostly non-uniform letter on number plate effects a lot in recognition. The license plate recognition
system mainly has four stages: image acquisition, license plate detection, character segmentation and character
recognition. The license plate recognition (LPR) system have many applications like payment of parking fees; toll
fee on the highway; traffic monitoring system; border security system; signal system etc.
In this paper, template matching algorithm for character recognition is used. The system presented here mainly
focuses on recognition of ambiguous characters based on position of the character. It is observed that the developed
system successfully detects & recognizes the vehicle number plate on real images and the problem of recognizing
ambiguous character is solved.
Localization of License Plate Number Using Dynamic Image Processing Techniq...KaashivInfoTech Company
In this research, the design of a new genetic algorithm (GA) is introduced to detect the locations of license plate (LP) symbols. An adaptive threshold method is applied to overcome the dynamic changes of illumination conditions when
Converting the image into binary. Connected component analysis technique (CCAT) is used to detect candidate objects inside the unknown image. A scale-invariant geometric relationship matrix is introduced to model the layout of symbols in any LP that simplifies system adaptability when applied in different
Countries. Moreover, two new crossover operators, based on sorting, are introduced, which greatly improve the convergence speed of the system. Most of the CCAT problems, such as touching or broken bodies, are minimized by modifying the GA to perform partial match until reaching an acceptable fitness
Value. The system is implemented using MATLAB and various image samples are experimented with to verify the distinction of the proposed system. Encouraging results with 98.4% overall accuracy are reported for two different datasets having variability in orientation, scaling, plate location, illumination, and complex
Background. Examples of distorted plate images are successfully detected due to the independency on the shape, color, or location of the plate.
Index Terms-Genetic algorithms (GAs), image processing, image representations, license plate detection, machine vision, road vehicle identification, sorting crossover.
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http://internshipinchennai.in/
http://inplant-training.org/
http://kernelmind.com/
http://inplanttraining-in-chennai.com/
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Automatic Number Plate Recognition (ANPR) is a highly accurate system capable of reading vehicle number plates without human intervention through the use of high speed image capture with supporting illumination, detection of characters within the images provided, verification of the character sequences as being those from a vehicle license plate, character recognition to convert image to text; so ending up with a set of metadata that identifies an image containing a vehicle license plate and the associated decoded text of that plate.
1) Implemented an algorithm based on Mexican hat operator and Euler number of binary image for vehicle license plate localization.
2) Back propagation artificial neural network classifier has been used to train and test the neural network based on the extracted feature.
3) A thorough testing of algorithm is performed on a database with varying illumination and plate conditions. Results are encouraging with success rate of 98.1% for license plate localization and 97.05% for character recognition.
Number plate identification perimeter protection system. Control which vehicle access your premise. Assign rules for vehicles trying to enter the premise. Blacklist vehicles. Generate Alert if a blacklisted or unregistered vehicle trying to enter the area. Make efficient use of your security staff
AUTOMATIC NUMBER PLATE RECOGNITION and Violation processing SECURAWorld
A.I and Surveillance for Smart cities
AUTOMATIC NUMBER PLATE RECOGNITION and Violation processing
A robust AI engine deeply trained to accurately detect, recognize a license plate that works with a variety of non standard formats as well.
An Intelligent system to automatically process challans wherever reads are deemed appropriate, reducing significant time and maximizing coverage.
Helmet Violation
Simultaneously identifying person not wearing helmet and it’s vehicle license plate, even if the person is on the back seat.
This AI, using deep learning methods is also able to identify ‘Triple Riding Violations’.
Smart Parking and Smart Anti-Hawking
Hawking Detection analytics is capable of detecting an illegally parked Hawking station out of the designated area. Resulting in much safer traffic.
Using neural networks + network of cameras to solve a simple yet complex parking problem across your whole campus, while also extracting various vehicle features (make, model, color, etc)
introduction to licence plate recognition technique, optical character recognition, functions used in the program, pros and cons, applications, future scope.
AUTOMATIC LICENSE PLATE RECOGNITION SYSTEM FOR INDIAN VEHICLE IDENTIFICATION ...Kuntal Bhowmick
Automatic License Plate Recognition (ANPR) is a practical application of image processing which uses number (license) plate is used to identify the vehicle. The aim is to design an efficient automatic vehicle identification system by using the
vehicle license plate. The system is implemented on the entrance for security control of a highly restricted area like
military zones or area around top government offices e.g.Parliament, Supreme Court etc.
It is worth mentioning that there is a scarcity in researches that introduce an automatic number plate recognition for indian vechicles.In this paper, a new algorithm is presented for Indian vehicle’s number plate recognition system. The proposed algorithm consists of two major parts: plate region extraction and plate recognition.Vehicle number plate region is extracted using the image segmentation in a vechicle image.Optical character recognition technique is used for the character recognition. And finally the resulting data is used to compare with the records on a database so as to come up with the specific information like the vehicle’s owner, registration state, address, etc.
The performance of the proposed algorithm has been tested on real license plate images of indian vechicles. Based on the experimental results, we noted that our algorithm shows superior performance special in number plate recognition phase.
Automatic license plate recognition system for indian vehicle identification ...Kuntal Bhowmick
Automatic License Plate Recognition (ANPR) is a practical application of image processing which uses number (license) plate is used to identify the vehicle. The aim is to design an efficient automatic vehicle identification system by using the
vehicle license plate. The system is implemented on the entrance for security control of a highly restricted area like
military zones or area around top government offices e.g.Parliament, Supreme Court etc.
It is worth mentioning that there is a scarcity in researches that introduce an automatic number plate recognition for indian vechicles.In this paper, a new algorithm is presented for Indian vehicle’s number plate recognition system. The proposed algorithm consists of two major parts: plate region extraction and plate recognition.Vehicle number plate region is extracted using the image segmentation in a vechicle image.Optical character recognition technique is used for the character recognition. And finally the resulting data is used to compare with the records on a database so as to come up with the specific information like the vehicle’s owner, registration state, address, etc.
The performance of the proposed algorithm has been tested on real license plate images of indian vechicles. Based on the experimental results, we noted that our algorithm shows superior performance special in number plate recognition phase.
Artificial Neural Network (ANN), Automatic Number Plate Recognition (ANPR), Character Segmentation,Edge detection, Extraction plate region, Image Segmentation, Number plate recognition, Number Plate, Optical Character Recognition.
Automatic License Plate Recognition Using Optical Character Recognition Based...IJARIIE JOURNAL
A License plate is a rectangular plate which is alphanumeric. The license plate is fixed on the vehicle and used to
identify the vehicle along with honor of that vehicle. There is a huge number of vehicles on the road so that traffic
control and vehicle owner identification has become a major problem.
The automatic number plate reorganization (ANPR) is one of the solutions of such kind of problem. There are
different methodologies but it is challenging task as some of the factors like high speed of vehicles, languages of
number plate & mostly non-uniform letter on number plate effects a lot in recognition. The license plate recognition
system mainly has four stages: image acquisition, license plate detection, character segmentation and character
recognition. The license plate recognition (LPR) system have many applications like payment of parking fees; toll
fee on the highway; traffic monitoring system; border security system; signal system etc.
In this paper, template matching algorithm for character recognition is used. The system presented here mainly
focuses on recognition of ambiguous characters based on position of the character. It is observed that the developed
system successfully detects & recognizes the vehicle number plate on real images and the problem of recognizing
ambiguous character is solved.
Localization of License Plate Number Using Dynamic Image Processing Techniq...KaashivInfoTech Company
In this research, the design of a new genetic algorithm (GA) is introduced to detect the locations of license plate (LP) symbols. An adaptive threshold method is applied to overcome the dynamic changes of illumination conditions when
Converting the image into binary. Connected component analysis technique (CCAT) is used to detect candidate objects inside the unknown image. A scale-invariant geometric relationship matrix is introduced to model the layout of symbols in any LP that simplifies system adaptability when applied in different
Countries. Moreover, two new crossover operators, based on sorting, are introduced, which greatly improve the convergence speed of the system. Most of the CCAT problems, such as touching or broken bodies, are minimized by modifying the GA to perform partial match until reaching an acceptable fitness
Value. The system is implemented using MATLAB and various image samples are experimented with to verify the distinction of the proposed system. Encouraging results with 98.4% overall accuracy are reported for two different datasets having variability in orientation, scaling, plate location, illumination, and complex
Background. Examples of distorted plate images are successfully detected due to the independency on the shape, color, or location of the plate.
Index Terms-Genetic algorithms (GAs), image processing, image representations, license plate detection, machine vision, road vehicle identification, sorting crossover.
http://kaashivinfotech.com/
http://inplanttrainingchennai.com/
http://inplanttraining-in-chennai.com/
http://internshipinchennai.in/
http://inplant-training.org/
http://kernelmind.com/
http://inplanttraining-in-chennai.com/
http://inplanttrainingchennai.com/
Automatic Number Plate Recognition (ANPR) is a highly accurate system capable of reading vehicle number plates without human intervention through the use of high speed image capture with supporting illumination, detection of characters within the images provided, verification of the character sequences as being those from a vehicle license plate, character recognition to convert image to text; so ending up with a set of metadata that identifies an image containing a vehicle license plate and the associated decoded text of that plate.
1) Implemented an algorithm based on Mexican hat operator and Euler number of binary image for vehicle license plate localization.
2) Back propagation artificial neural network classifier has been used to train and test the neural network based on the extracted feature.
3) A thorough testing of algorithm is performed on a database with varying illumination and plate conditions. Results are encouraging with success rate of 98.1% for license plate localization and 97.05% for character recognition.
Lecture 7 from a course on Mobile Based Augmented Reality Development taught by Mark Billinghurst and Zi Siang See on November 29th and 30th 2015 at Johor Bahru in Malaysia. This lecture shows how to use Unity 3D and Vuforia to make mobile AR applications. Look for the other 9 lectures in the course.
This presentation gives detailed overview of Android, Android Architecture, Software Stack, Platform, Database Support, Licensing, File System, Network Connectivity, Security and Permissions, IDE and Tools, Other IDEs Overview, Development Evaluation, Singing your application, Versioning your application, Preparing to publish your application, Publish your App on Android Market. This presentation also includes links to sample exampled.
Note: Few slides from this presentation are taken from internet or slideshare.com as it is or modified little bit. I have no intention of saying someone’s else work as mine. I prepared this presentation to just educate co-workers about android. So I want the best material from internet and slideshare.com.
Abstract:
With an everyday increase in the number of cars on our roads and highways, we are facing numerous problems, for example:
• Smuggling of cars
• Invalid license plates
• Identification of stolen cars
• Usage of cars in terrorist attacks/illegal activities
In order to address the above issues, we took up the project of developing a prototype, which can perform license plate recognition (LPR). This project, as the name signifies, deals with reading, storing and comparing the license plate numbers retrieved from snapshots of cars to ensure safety in the country and ultimately help to reduce unauthorized vehicles access and crime.
License Plate Recognition (LPR) has been a practical technique in the past decades. It is one of the most important applications for Computer Vision, Patter Recognition and Image Processing in the field of Intelligent Transportation Systems (ITS).
Generally, the LPR system is divided into three steps, license plate locating, license plate character segmentation and license plate recognition. This project discusses a complete license plate recognition system with special emphasis on the Localization Module.In this study, the proposed algorithm is based on extraction of plate region using morphological operations and shape detection algorithms. Segmentation of plate made use of horizontal and vertical smearing and line detection algorithms. Lastly, template matching algorithms were used for character recognition.
The implementation of the project was done in the platforms of Matlab and OpenCV.
Tracking number plate from vehicle usingijfcstjournal
In Traffic surveillance, Tracking of the number plate from the vehicle is an important task, which demands
intelligent solution. In this document, extraction and Recognization of number plate from vehicles image
has been done using Matlab. It is assumed that images of the vehicle have been captured from Digital
Camera. Alphanumeric Characters on plate has been Extracted and recognized using template images of
alphanumeric characters.
This paper presents a new algorithm in MATLAB which has been used to extract the number plate from the
vehicle in various luminance conditions. Extracted image of the number plate can be seen in a text file for
verification purpose. Number plate identification is helpful in finding stolen cars, car parking management
system and identification of vehicle in traffic.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A License plate is a rectangular plate which is alphanumeric. The license plate is fixed on the vehicle and used to identify the
vehicle along with honor of that vehicle. There is a huge nos. of vehicles are on the road word wile so that traffic control and
vehicle owner identification has become a major problem.
The automatic number plate reorganization (ANPR) is one of the solutions of such kind of problem. There is nos. of methodologies
but it is challenging task as some of the factors like high speed of vehicles, languages of number plate & mostly non-uniform
letter on number plate effects a lot in recognition. The license plate recognition (LPR) system have many application like payment
of parking fees; toll fee on highway; traffic monitoring system; border security system; signal system etc.
In this paper, the different method of license plate recognition is discussed. The systems first detects the vehicle and capture the
image then the number plate of vehicle is extracted from the image using image Segmentation optical character recognition technique
is used for the character recognition. Then the resulting date is compared with the database record so we come up the information
like the vehicle’s owner, vehicle registration place, address etc. it is observed that developed system successfully defect
& recognize the vehicle number plate on real image.
An Efficient Model to Identify A Vehicle by Recognizing the Alphanumeric Char...IJMTST Journal
Automatic Engine Number Recognition (AENR) is the digital image processing and an important aspect/role to identify the theft vehicles by recognizing characters, digits and special symbols. There is increase in the theft of vehicles, so to identify these theft vehicles, the proposed system is introduced. The proposed system controls the theft vehicles by recognizing a digits and characters in the number plate and chassis region and stores in the database in ASCII format to check the theft vehicles are registered or unregistered. Both system consists of 4 common phases: - Preprocessing, Character Extraction (ROI), Character Segmentation, and Character Recognition. This paper proposes a new scheme for engine number and chassis number extraction from the pre-processed image of the vehicle’s engine and chassis region using preprocess techniques, Region of Interest(ROI), Binarization, thresholding, template matching.
License Plate Recognition using Morphological Operation. Amitava Choudhury
This paper describes an efficient technique of locating and
extracting license plate and recognizing each segmented
character. The proposed model can be subdivided into four
parts- Digitization of image, Edge Detection, Separation of
characters and Template Matching. In this work, we propose a
method which is based on morphological operations where
different Structuring Elements (SE) are used to maximally
eliminate non-plate region and enhance plate region.
Character segmentation is done using Connected Component
Analysis. Correlation based template matching technique is
used for recognition of characters. This system is
implemented using MATLAB7.4.0. The proposed system is
mainly applicable to Indian License Plates.
A design of license plate recognition system using convolutional neural networkIJECEIAES
This paper proposes an improved Convolutional Neural Network (CNN) algorithm approach for license plate recognition system. The main contribution of this work is on the methodology to determine the best model for four-layered CNN architecture that has been used as the recognition method. This is achieved by validating the best parameters of the enhanced Stochastic Diagonal Levenberg Marquardt (SDLM) learning algorithm and network size of CNN. Several preprocessing algorithms such as Sobel operator edge detection, morphological operation and connected component analysis have been used to localize the license plate, isolate and segment the characters respectively before feeding the input to CNN. It is found that the proposed model is superior when subjected to multi-scaling and variations of input patterns. As a result, the license plate preprocessing stage achieved 74.7% accuracy and CNN recognition stage achieved 94.6% accuracy.
Automatic Car Number Plate Detection and Recognition using MATLABHimanshiSingh71
Car Number Plate Recognition and Detection (ANPRD) using MATLAB. This is MATLAB based project.
Take an input from user than convert it into gray scale image and then applying morphological operations and many more functions.
Mobile Based Application to Scan the Number Plate and To Verify the Owner Det...inventionjournals
Any License plate recognition system usually passes through three steps of image processing: 1) Extraction of a license plate region; 2) Segmentation of the plate characters; and 3) Recognition of each character. A number of algorithms have been proposed in recent times for efficient disposal of the application. The purpose of this project was to develop a real time application which recognizes number plates from cars at a gate, for example at the entrance of a parking area or a border crossing. The system, based on regular PC with mobile camera, catches video frames which include a visible car number plate and processes them. Once a number plate is detected, its digits are recognized, displayed on the User Interface or checked against a database.The software aspect of the system runs on mobile hardware and can be linked to other applications or databases. It first uses a series of image manipulation techniques to detect, normalize and enhance the Image of the number plate, and then optical character recognition (ocr) to extract the alpha numeric text of number plate. The system are generally deployed in one of two basic approaches: one allows for the entire process to be performed at the lane location in real-time. The other will reveal the driver’s profile by checking in the registered database.
OCR optimization for vehicle number plate Identification based on Template ma...IJEEE
Optical character recognition (OCR) is an approach to extract the characters from an image. Vehicle number plate identification is already a challenging task in OCR. In this paper a method for vehicle license plate identification is implemented and analyzed, on the basis of novel adaptive image segmentation and filtering technique conjunction with optical character recognition has been proposed. In this paper a novel method for license plate number localization based on ratio and position of characters is performed. The localized characters have been correlated to the predefined templates of characters. Based on appropriate threshold of character authentication, the correlation value decides the valid character for localized region of interest. This paper is divided into five segments: first part consists of introduction and literature survey, second part deals with image conversion (from RGB to black and white), removal of unwanted noisy region and classification of connected components, third part explains filtering based on ratio of height to width for validation of true character using height filter and position filter, fourth part explains how to extract the likelihood region of character using median centroid approach for number plate. This approach enables the localization of number plates in widely varying illumination conditions with relevance to the number plate having English alphanumeric fonts. Fifth part of this paper explains the correlation with each templates for validate the character based on maximum correlation value.
Similar to Smart License Plate Recognition System based on Image Processing (20)
Due to availability of internet and evolution of embedded devices, Internet of things can be useful to contribute in energy domain. The Internet of Things (IoT) will deliver a smarter grid to enable more information and connectivity throughout the infrastructure and to homes. Through the IoT, consumers, manufacturers and utility providers will come across new ways to manage devices and ultimately conserve resources and save money by using smart meters, home gateways, smart plugs and connected appliances. The future smart home, various devices will be able to measure and share their energy consumption, and actively participate in house-wide or building wide energy management systems. This paper discusses the different approaches being taken worldwide to connect the smart grid. Full system solutions can be developed by combining hardware and software to address some of the challenges in building a smarter and more connected smart grid.
A Survey Report on : Security & Challenges in Internet of Thingsijsrd.com
In the era of computing technology, Internet of Things (IoT) devices are now popular in each and every domains like e-governance, e-Health, e-Home, e-Commerce, and e-Trafficking etc. Iot is spreading from small to large applications in all fields like Smart Cities, Smart Grids, Smart Transportation. As on one side IoT provide facilities and services for the society. On the other hand, IoT security is also a crucial issues.IoT security is an area which totally concerned for giving security to connected devices and networks in the IoT .As, IoT is vast area with usability, performance, security, and reliability as a major challenges in it. The growth of the IoT is exponentially increases as driven by market pressures, which proportionally increases the security threats involved in IoT The relationship between the security and billions of devices connecting to the Internet cannot be described with existing mathematical methods. In this paper, we explore the opportunities possible in the IoT with security threats and challenges associated with it.
In today’s emerging world of Internet, each and every thing is supposed to be in connected mode with the help of billions of smart devices. By connecting all the devises used in our day to day life, make our life trouble less and easy. We are incorporated in a world where we are used to have smart phones, smart cars, smart gadgets, smart homes and smart cities. Different institutes and researchers are working for creating a smart world for us but real question which we need to emphasis on is how to make dumb devises talk with uncommon hardware and communication technology. For the same what kind of mechanism to use with various protocols and less human interaction. The purpose is to provide the key area for application of IoT and a platform on which various devices having different mechanism and protocols can communicate with an integrated architecture.
Study on Issues in Managing and Protecting Data of IOTijsrd.com
This paper discusses variety of issues for preserving and managing data produced by IoT. Every second large amount of data are added or updated in the IoT databases across the heterogeneous environment. While managing the data each phase of data processing for IoT data is exigent like storing data, querying, indexing, transaction management and failure handling. We also refer to the problem of data integration and protection as data requires to be fit in single layout and travel securely as they arrive in the pool from diversified sources in different structure. Finally, we confer a standardized pathway to manage and to defend data in consistent manner.
Interactive Technologies for Improving Quality of Education to Build Collabor...ijsrd.com
Today with advancement in Information Communication Technology (ICT) the way the education is being delivered is seeing a paradigm shift from boring classroom lectures to interactive applications such as 2-D and 3-D learning content, animations, live videos, response systems, interactive panels, education games, virtual laboratories and collaborative research (data gathering and analysis) etc. Engineering is emerging with more innovative solutions in the field of education and bringing out their innovative products to improve education delivery. The academic institutes which were once hesitant to use such technology are now looking forward to such innovations. They are adopting the new ways as they are realizing the vast benefits of using such methods and technology. The benefits are better comprehensibility, improved learning efficiency of students, and access to vast knowledge resources, geographical reach, quick feedback, accountability and quality research. This paper focuses on how engineering can leverage the latest technology and build a collaborative learning environment which can then be integrated with the national e-learning grid.
Internet of Things - Paradigm Shift of Future Internet Application for Specia...ijsrd.com
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Smart License Plate Recognition System based on Image Processing
1. IJSRD - International Journal for Scientific Research & Development| Vol. 1, Issue 9, 2013 | ISSN (online): 2321-0613
All rights reserved by www.ijsrd.com 1864
Abstract—This report describes the Smart License Plate
Reorganization System, which can be installed into a
tollbooth for automated acceptation of vehicle license plate
details using an image of a vehicle. This Smart License Plate
Reorganization system could then be implemented to control
the payment of fees, highways, bridges, parking areas or
tunnels, etc. This report contains new algorithm for
acceptation number plate using Structural operation,
Thresholding operation, Edge detection, Bounding box
analysis for number plate extraction, character separation
using separation and character acceptation using Template
method and Feature extraction.
I. INTRODUCTION
Smart License Plate recognition (SLPR) system is an
important technique which can be used in Intelligent
Transportation System (ITS). SLPR is an advanced machine
vision technology used to identify vehicles by their number
plates in which there is no need any human intervention. It
is a very important area of research due to its many
applications. The development of Intelligent Transportation
System (ITS) provides the data of vehicle numbers which
can be used in analyses and monitoring. SLPR is important
in the area of highway toll collection, traffic problems,
borders and custom security, premises where high security is
needed, like Legislative Assembly, Parliament, and so on.
Smart License Plate recognition (SLPR) is a form of
automatic vehicle identification. It is an image processing
technology used to identify vehicles with the help of only
their license plates. SLPR plays a major role in automatic
monitoring of traffic rules and maintaining law coercion on
public roads. Since every vehicle carries a predicate license
plate, no any external cards, tags or transmitters need to be
perceptible, only license plate perceptible.
The complexity of smart license number plate
acceptation work varies throughout the world. For the
standard number plate, SLPR system is easier to read and
recognize.
In our country India this task much more difficult
because of variation in plate model and their size. Character
acceptation part is also very difficult in Indian number plate.
So flexible algorithm required for solved this task.
SLPR system consists of following modules: 1)
Extraction of number plate, 2) Character separation, and 3)
Character acceptation. In this report, SLPR work for our
Indian car. Images are carrying out with different
illumination conditions, different background and
orientation. To take care of lighting and contrast properly
Histogram equalization and median filter are used. Sobel
vertical edge notation and structurally is employed to
locate the number plate. To separate out the characters
Fig. 1: Flowchart of SLPR Algorithm
Present on the number plate Projection analysis is used. For
acceptation work template is used. .The rest of the report is
organized as follows: Section 2 shows the Literature
survey; Section 3 explains the projected algorithm for the
use of number plate extraction; Section 4 explains the
algorithm used for character separation; Section 5 describes
the number plate acceptation algorithm using template of
Smart License Plate Recognition System based on
Image Processing
Jatin M. Patel1
R. K. Somani2
Pankaj Singh Parihar3
1, 2, 3
Department of Computer science & Engineering
1, 2, 3
Institute of Technology & Management, Bhilawara, RTU, Rajasthan, India
2. Smart License Plate Recognition System based on Image Processing
(IJSRD/Vol. 1/Issue 9/2013/0044)
All rights reserved by www.ijsrd.com 1865
character. Section 6 shows the Experimental results and
Section 7 conclusion.
II. NUMBER PLATE EXTRACTION
Number plate extraction is the very important step in SLPR
system which affects the accuracy of the system
significantly. Basically extraction of number plate is
difficult task due to many problems like Number plates
generally occupy only a small portion from the whole
image; there are many difference in number plate formats,
and bear upon of environmental factors. This step affects the
accuracy of character separation and acceptation work.
There are Different types of techniques are developed for
number plate extraction. The final goal of this phase, we
have to give only an input image with the use of this input
image detect only the region which contain the number plate
of vehicle and it is validate for true number plate.
Image Acquisition and Pre-processingA.
In this system we are using high resolution camera to get an
image. Here images are taken in different illumination
conditions, background and at various distances from the
camera to vehicle. Image converted into gray scale from
RGB. Now all next steps are penalize on gray scale image.
Figure 2(a) shows gray scale image. With the use of pre-
processing we can raise the processing speed, improve the
contrast of the image and we can also reduce the noise in the
image.
Structural OperationB.
Structural opening operation used using structure element on
vehicle gray scale image then subtracted from original
image. Into this operation remove pixel which has less
radius than disk radius with the help of disk-shaped
structuring element. So the number plate and light which has
less disk radius are remaining in next phase and the other
part which is not necessary remove from the image.
Thresholding OperationC.
With the use of Threshold operation we get binary image
from gray scale by calculating level of threshold. First we
have to find the minimum and maximum value of pixel from
image. While we are performing this operation all pixel is
converted in 0-1 form so the next processing simple
Vertical Edge notationD.
The characters which are on number plate area contain
abundant edges as compared to background area. This
feature is utilized for locating the candidate plate area from
the input image.
Fig. 2: Gray scale image
Fig. 3: Effect of Threshold
Fig. 4: Sobel vertical edge detection
Sobel vertical edge notation is used to find out the regions
which have high pixel variance value [8]. Thresholding
operation is used to select rows which have particular white
pixel density. With the use of this operation we can extract
candidate number plate region from the whole image. From
the Fig. 2(c) we can see the result of Sobel vertical edge
notation algorithm.
Candidate Plate Area NotationE.
The aim of structural operation is that remove unnecessary
objects which is not require in the image. To separate
candidate plate areas from the whole image opening and
closing operation are used. In some cases background areas
of the image also acquire as a candidate plate. So to remove
the forge candidates, plate validation is require and it is done
using aspect ratio of the number plate horizontal cuts [11] in
the number plate.
True Number Plate ExtractionF.
When the notation of candidate number plate area is done
Bounding Box analysis is Require. Which is used to
separate out plate area from the whole image From the
Bounding Box analysis, respective row and column indices
of plate area are found out? Once the indices of number
plate are known, the number plate is takeout from original
gray scale image. The result is as shown in Fig. 5
Fig. 5: Extracted true number plate.
2.7 Adjust Number Plate in appropriate angle
The operation adjust number plate is used when number
plate is not in proper way. So this operation performs
rotating operation for true acceptation. Fig 5 shows the
result of this operation. This operation perform sacrificial
neural networking in which find two points from the left
side and right side of the number plate and calculate the slop
equation.
The equation is shown below.
M = (y2-y1) / (x2-x1)
3. Smart License Plate Recognition System based on Image Processing
(IJSRD/Vol. 1/Issue 9/2013/0044)
All rights reserved by www.ijsrd.com 1866
Theta = tan-1(M).
Where, M is slop and Theta is rotation angle.
Fig. 6: Extracted plate, Rotated plate
III. CHARACTER SEGMENTATION
The important step of SLPR system is character isolation
from the number plate area, which touch the accuracy of
character acceptation significantly. Given the number plate
image, is to separate all the characters, without losing any
feature of character from the number plate is the final goal
of this phase. In to this phase there is a sequence of
operation like character region enhancement, connected
component analysis is and projection analysis.
Character Region EnhancementA.
Fig. 7: Binarized number plate
Into this phase the image of number plate is converted into
binary image which is done with the use of graythresh
function. Here Gnaw operation is used for focusing each and
every character from the number plate. Gnaw operation
made every character thick because all character in black
colour. Fig 5 and Fig 8 show the result of Binaries image
and Gnaw image simultaneously.
Connected Component AnalysisB.
Then necessary noise from the number plate
is removed into the connected component analysis. As per
the based on the area of threshold every matrix of 8 pixels is
evaluated. Into the fig 8 we can see the result of connected
component analysis and noise removal.
Fig. 8: Gnaw image, Converted Black to white, connected
component analysis
Vertical Projection AnalysisC.
Now the final operation in SLPR is to separate out each
character from the number plate and take into one text file. This
operation is performing into character separation phase. Now to
find out the gaps between two character vertical projections
analysis is used. As per the based on the number of projection
all character are separated. Each character's Row and column
indices are recorded and it is takeout from the original gray
scale number plate. This complete operation is performing to
isolate every character from the number plate which is
horizontally in one row. The result of this operation is shown in
fig 4
IV. CHARACTER RECOGNITION
The character acceptation phase consists of two Method: 1)
Template Comparing, 2) Feature Extraction.
Template Comparing [25]A.
The character acceptation phase consists of following
steps: 1) Lading template 2) Character normalization, 3)
Comparing character with the Template comparing
1) Lading Template
In to this operation loads a template of character. We are
taking 24 X 42 pixel A to Z alphabet and 0 to 9 number
images. Here read all image and it is store in our database.
So there are total 36 characters in database. This template
work as global.
Character NormalizationB.
Now when we separate out the character at that time there
is too much variation in size of character. So into this phase
all character is normalizing into predefined size in pixel.
Each character is normalized into a size of 24 X
42 with the use of image mapping technique.
1) Template comparing
Now this task is perform with the database in which
normalized character is compare with each template
character image and finds the matching between separated
character and template character. After performing this task
selecting the most relevant image and it is written into text
file. Fig 6 shows text file which is contain output of
number plate.
Fig. 9: Character 24x42 size image
Feature based character AcceptationC.
Now this phase is little bit difficult. Into this phase there are
two purposes. 1) To extract property which is used to
identify a character uniquely 2) To extract properties this
can differentiate between similar characters. Now the
problem is that character can be written in a different way
and yet it can be easily recognized by a Human. Thus, there
exist a set of principles or logics that stand out all
differences. Now the futures work is that the system has
4. Smart License Plate Recognition System based on Image Processing
(IJSRD/Vol. 1/Issue 9/2013/0044)
All rights reserved by www.ijsrd.com 1867
properties which are close to the psychology of the
characters.
1) Regularizing Feature Extraction [27]
Now into this phase every character is usually divided into
zones of predefined size. Into this predefined grid sizes are
in the order of 3x3, 4x4 etc. Fig 5 shows the result of
regularizing of a character. By considering the bottom left
corner of each image as the absolute origin (0, 0), the phase
angle of each grid at {x, y} is computed as below
( )
Where {x, y} is top right corner point of each grid. So theta
is difference for each zone.
While computing any feature value, each pixel
contribution utilizes this value to make it a unique
contribution. For instance, the box feature of a grid is
computed as follows. By considering the bottom left corner
as the absolute origin (0, 0), the coordinate distance (Vector
Distance) for the kth
pixel in the bth
box at location (i, j) is
computed as:
( )
By dividing the sum of distances of all black pixels present
in a box with their total number, a modified box feature is
obtained (λ) for each box as follows
∑
Where N is total number of pixels in a box. nb is the number
of black (pattern) pixels in bth
box.
The feature of each zone is computed as follows
Where α is a multiplying factor taken as 1 in our
experiments .As the range of COS θ is 0 to1 only, α was
introduced
2) Regional Feature extraction
The following regional features are Takeout from character
image
1) 'Euler Number' — Scalar that specifies the number of
objects in the region minus the number of holes in those
objects. This property is supported only for 2-D input
label matrices. Region props use 8-connectivity to
compute the Euler Number measurement.
2) 'Eccentricity' — Scalar that specifies the eccentricity of
the ellipse that has the same second-moments as the
region. The eccentricity is the ratio of the distance
between the foci of the ellipse and its major axis length.
The value is between 0 and 1. (0 and 1 are degenerate
cases; an ellipse whose eccentricity is 0 is actually a
circle, while an ellipse whose eccentricity is 1 is a line
separate.) This property is supported only for 2-D input
label matrices.
3) 'Extent' — Scalar that specifies the ratio of pixels in the
region to pixels in the total bounding box. Computed as
the Area divided by the area of the bounding box. This
property is supported only for 2-D input label matrices.
4) 'Orientation' — Scalar; the angle (in degrees ranging
from -90 to 90 degrees) between the x-axis and the
major axis of the ellipse that has the same second-
moments as the region. This property is supported only
for 2-D input label matrices.
5) 'Convex Area' — Scalar that specifies the number of
pixels in 'Convex Image'. This property is supported
only for 2-D input label matrices.
6) 'Filled Area' — Scalar specifying the number of on
pixels in Filled Image.
7) Major Axis Length' — Scalar specifying the length (in
pixels) of the major axis of the ellipse that has the same
normalized second central moments as the region. This
property is supported only for 2-D input label matrices.
8) 'Minor Axis Length' — Scalar; the length (in pixels) of
the minor axis of the ellipse that has the same
normalized second central moments as the region. This
property is supported only for 2-D input label matrices.
3) ARTIFICIAL NEURAL NETWORKS Training and
Classification [26]
In SLPR sequence of character of license plate identifies the
vehicle. Now here we are using artificial neural network for
recognize of license plate characters. There is a one of the
best advantages of Neural Network are existing correlate in
and statistics template techniques which is use to stable to
noises and some modification in the position of characters of
number plate. The ARTIFICIAL NEURAL NETWORKS is
‘trained’ before the character acceptation can take place so it
can improve the capability of mapping various inputs to the
required outputs and it can effectively classify various
characters. We use the 'Vectors' which is generated by the
'Database templates' with the use of Feature Extraction
techniques for training the ARTIFICIAL NEURAL
NETWORKS. As we have above discuss regularizing
feature and many different types of feature, all are used to
generate 17 parameters, which is use in ARTIFICIAL
NEURAL NETWORKS (ANN). In Neural network we are
using 10 types of every character so total number of images
in our database are 360. So there are total 17x360 values in
to the ANN to receive 36 deferent values at the output side.
Here we have to note down that Back propagation algorithm
is used in the ARTIFICIAL NEURAL NETWORK for
Learning. System programmer specify the 'target' value so
we can accommodate at the small acceptation errors, which
may be change from one application to another application.
Fig. 10: Neural Network
The ARTIFICIAL NEURAL NETWORK is good for much
Condition and it is very reliable for much iteration and to
complete the process it takes around 21 seconds. Here we
are using ‘Sum Squared Error ‘for Training function in place
of ‘Mean Squared Error’ because the system calculate the
effect of joint errors for all the parameters in place of all
over errors. The learning rate of ANN is 0.01. An error goal
of 1e-1 was achieved by the ANN.
5. Smart License Plate Recognition System based on Image Processing
(IJSRD/Vol. 1/Issue 9/2013/0044)
All rights reserved by www.ijsrd.com 1868
4) Analysis of different hidden neural
ARTIFICIAL NEURAL NETWORKS has three layer input,
hidden and output shown in figure 8. Here problem is how
to defined number of hidden neuron in our implementation,
so we have taken different number of neuron and done
analysis using Regional and regularizing feature. Table 1
shown analysis of number of neuron with correct character
acceptation and time analysis using regional feature and
regional + regularizing feature. Table shows that training
time is increase with increased number of hidden neuron and
number of correct character acceptation increase with
increased number of hidden neuron. Here analysis shows
that when we have used only regional feature then all
character correctly recognized using 600 hidden neuron and
when we have used regularizing feature with regional
feature then all character correctly recognized using 300
hidden neuron which is half of previous. Time required for
correct acceptation using only regional feature is 38.2 secs
and time required for correct acceptation using regional +
regularizing feature is 17.6 secs. This analysis shows that
when we have taken more and more feature its decrease
number of hidden neuron for correct acceptation and make
algorithm faster because less hidden neuron take less
training time.
V. EXPERIMENTAL RESULTS AND PERFORMANCE
ANALYSIS
A database consists of different sized JPEG coloured
images. Total 150 images are used to test the algorithm. The
images are taken with different background as well as
illumination conditions. Experiments show that the
algorithm has good performance on number plate extraction,
and character separation work. It can deal the images
correctly, with noise, illumination variance, and rotation to
±50. This work is implemented using MATLAB. Table 6.1
illustrates number plate extraction and character separation
success rate.
Algorithms
Total no.
of image
Success
rate (%)
Number plate Extraction 135 90
Character separation 127 85
Character Acceptation using
Template based
120 80
Character Acceptation using
feature based
124 84
Table. 1: number plate extraction and character separation
success rate
Deep shadows and reflections have an impact on number
plate extraction work. Because of uneven illumination,
stained number plates, true number plates could not get
correctly Takeout. Failure in character separation was
mainly because of merging of characters on number plate,
stained number plates, orientation of the image and poor
Illumination.
Character acceptation work is done on 10 digits (0
to 9) and 26 alphabets (A to Z). The acceptation rate
achieved is 80% using Template comparing but 84% using
feature Extraction. This analysis show that feature extraction
method using neural network give good performance then
direct template comparing. In feature extraction method
training is done by neural network so efficiency of result
increases. In template comparing false acceptation is due to
similarity in the character shape, e.g. 6 and B, 5 and S etc.
but when we extract feature from character image the false
recognition is removed because different type of same
character is trained and it is recognized correctly.
VI. CONCLUSIONS & FUTURE WORKS
ConclusionsA.
An algorithm for vehicle number plate extraction, character
separation and acceptation is presented. Database of the
image consists of images with different size, background,
illumination, camera angle, distance etc. The experimental
results show that, number plates are Takeout faithfully based
on vertical edge notation and connected component
algorithm, with the success rate of 90%. Character
separation phase using connected component analysis and
vertical projection analysis works well with the success rate
of 85%. Feature based character acceptation based on neural
network give better result than template method. The
success rate achieved for character acceptation is 84%.
Future worksB.
In future we have to find maximum feature of character and
improved our result in character acceptation.
There are still some further researches to do. For example, it
can’t work with some other kind of license plate, such as
two-row plate. This problem will be solved in the further
work.
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