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.
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 %.
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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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 %.
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://inplanttraining-in-chennai.com/
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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.
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.
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
Automatic License Plate Recognition using OpenCVEditor IJCATR
Automatic License Plate Recognition system is a real time embedded system which automatically recognizes the license plate of vehicles. There are many applications ranging from complex security systems to common areas and from parking admission to urban traffic control. Automatic license plate recognition (ALPR) has complex characteristics due to diverse effects such as of light and speed. Most of the ALPR systems are built using proprietary tools like Matlab. This paper presents an alternative method of implementing ALPR systems using Free Software including Python and the Open Computer Vision Library.
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.
License Plate Recognition System using Python and OpenCVVishal Polley
License plate recognition (LPR) is a type of technology, mainly software, that enables computer systems to read automatically the registration number (license number) of vehicles from digital pictures.
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.
The ANPR (Automatic Number Plate Recognition) using ALR (Automatic line
Tracking Robot) is a system designed to help in recognition of number plates of vehicles.
This system is designed for the purpose of the security and it is a security system.
For more details
http://projectsofashok.blogspot.com/2010/04/anprautomatic-number-plate-recognition.html
Vehicle Identification and Classification SystemVishal Polley
The VICS system for identification and classification of moving vehicles on the road side from the videos is a great importance today. The main goal of our project is to implement an efficient method for recognizing vehicles in Indian conditions.
Vehicle plate recognition is a successful image processing technique used to recognize vehicles' plate numbers. There are several applications for this method which enlarge through many fields and attention groups. Vehicle plate recognition may be considered as an advertising equipment, for the purpose of traffic and border securities for law enforcement, and travel. Many methods have been accompanied to make this technique easy. This learning proposes an edge-detection method to allow a Plate Recognition System of a vehicle through the practical situations like the various environmental or meteorological conditions. Image processing tools are used to examine the plate area, resize it, and change it on the way to a gray scale earlier to filtering of the image in order to remove the unwanted areas. The obtained objects is processed in such a way that the number plate image and the information related to that is completely perfect The information of the obtained image is processed through the average deviation of the Gaussian filter (sigma).
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.
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.
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.
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
Automatic License Plate Recognition using OpenCVEditor IJCATR
Automatic License Plate Recognition system is a real time embedded system which automatically recognizes the license plate of vehicles. There are many applications ranging from complex security systems to common areas and from parking admission to urban traffic control. Automatic license plate recognition (ALPR) has complex characteristics due to diverse effects such as of light and speed. Most of the ALPR systems are built using proprietary tools like Matlab. This paper presents an alternative method of implementing ALPR systems using Free Software including Python and the Open Computer Vision Library.
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.
License Plate Recognition System using Python and OpenCVVishal Polley
License plate recognition (LPR) is a type of technology, mainly software, that enables computer systems to read automatically the registration number (license number) of vehicles from digital pictures.
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.
The ANPR (Automatic Number Plate Recognition) using ALR (Automatic line
Tracking Robot) is a system designed to help in recognition of number plates of vehicles.
This system is designed for the purpose of the security and it is a security system.
For more details
http://projectsofashok.blogspot.com/2010/04/anprautomatic-number-plate-recognition.html
Vehicle Identification and Classification SystemVishal Polley
The VICS system for identification and classification of moving vehicles on the road side from the videos is a great importance today. The main goal of our project is to implement an efficient method for recognizing vehicles in Indian conditions.
Vehicle plate recognition is a successful image processing technique used to recognize vehicles' plate numbers. There are several applications for this method which enlarge through many fields and attention groups. Vehicle plate recognition may be considered as an advertising equipment, for the purpose of traffic and border securities for law enforcement, and travel. Many methods have been accompanied to make this technique easy. This learning proposes an edge-detection method to allow a Plate Recognition System of a vehicle through the practical situations like the various environmental or meteorological conditions. Image processing tools are used to examine the plate area, resize it, and change it on the way to a gray scale earlier to filtering of the image in order to remove the unwanted areas. The obtained objects is processed in such a way that the number plate image and the information related to that is completely perfect The information of the obtained image is processed through the average deviation of the Gaussian filter (sigma).
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.
The automatic license plate recognition(alpr)eSAT Journals
Abstract Every country uses their own way of designing and allocating number plates to their country vehicles. This license number plate is then used by various government offices for their respective regular administrative task like- traffic police tracking the people who are violating the traffic rules, to identify the theft cars, in toll collection and parking allocation management etc. In India all motorized vehicle are assigned unique numbers. These numbers are assigned to the vehicles by district-level Regional Transport Office (RTO). In India the license plates must be kept in both front and back of the vehicle. These plates in general are easily readable by human due to their high level of intelligence on the contrary; it becomes an extremely difficult task for the computers to do the same. Many attributes like illumination, blur, background color, foreground color etc. will pose a problem. Index Terms: Automatic license plate recognition (ALPR) system, proposed methodology, reference
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.
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.
Nowadays character recognition has gained lot of attention in the field of pattern recognition due to its application in various fields. It is one of the most successful applications of automatic pattern recognition. Research in OCR is popular for its application potential in banks, post offices, office automation etc. HCR is useful in cheque processing in banks; almost all kind of form processing systems, handwritten postal address resolution and many more. This paper presents a simple and efficient approach for the implementation of OCR and translation of scanned images of printed text into machine-encoded text. It makes use of different image analysis phases followed by image detection via pre-processing and post-processing. This paper also describes scanning the entire document (same as the segmentation in our case) and recognizing individual characters from image irrespective of their position, size and various font styles and it deals with recognition of the symbols from English language, which is internationally accepted.
COPY TEXT FROM IMAGE - Optical Character Recognition (OCR) kannan1171
In several situations we end up in searching for an original document of an Image file to copy junk of texts characters/data for compiling/generating a new document (CAD drafting or MS Office) and end up in not getting the original file.
In that scenario the only solution would be retyping the whole text data and it take a lot of unproductive time.
Please practice this procedure whenever similar situation arises and increase your productivity of document generation by copying the required text details from existing image files like .tiff, .jpg, .bmp, .pdf etc.
Design and implementation of optical character recognition using template mat...eSAT Journals
Abstract
Optical character recognition (OCR) is an efficient way of converting scanned image into machine code which can further edit. There are variety of methods have been implemented in the field of character recognition. This paper proposes Optical character recognition by using Template Matching. The templates formed, having variety of fonts and size .In this proposed system, Image pre-processing, Feature extraction and classification algorithms have been implemented so as to build an excellent character recognition technique for different scripts .Result of this approach is also discussed in this paper. This system is implemented in Matlab.
Keywords- OCR, Feature Extraction, Classification
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.
A Novel Multiple License Plate Extraction Technique for Complex Background in...CSCJournals
License plate recognition (LPR) is one of the most important applications of applying computer techniques towards intelligent transportation systems (ITS). In order to recognize a license plate efficiently, location and extraction of the license plate is the key step. Hence finding the position of a license plate in a vehicle image is considered to be the most crucial step of an LPR system, and this in turn greatly affects the recognition rate and overall speed of the whole system. This paper mainly deals with the detecting license plate location issues in Indian traffic conditions. The vehicles in India sometimes bare extra textual regions such as owner’s name, symbols, popular sayings and advertisement boards in addition to license plate. Situation insists for accurate discrimination of text class and fine aspect ratio analysis. In addition to this additional care taken up in this paper is to extract license plate of motorcycle (size of plate is small and double row plate), car (single as well as double row type), transport system such as bus, truck, (dirty plates) as well as multiple license plates present in an image frame under consideration. Disparity of aspect ratios is a typical feature of Indian traffic. Proposed method aims at identifying region of interest by performing a sequence of directional segmentation and morphological processing. Always the first step is of contrast enhancement, which is accomplished by using sigmoid function. In the subsequent steps, connected component analysis followed by different filtering techniques like aspect ratio analysis and plate compatible filter technique is used to find exact license plate. The proposed method is tested on large database consisting of 750 images taken in different conditions. The algorithm could detect the license plate in 742 images with success rate of 99.2%.
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.
An Intelligent Control System Using an Efficient License Plate Location and R...CSCJournals
This paper presents a real-time and robust method for license plate location and recognition. After adjusting the image intensity values, an optimal adaptive threshold is found to detect car edges and then algorithm used morphological operators to make candidate regions. Features of each region are to be extracted in order to correctly differentiate the license plate regions from others. It was done by analysis of percentage of Rectangularity of plate in decision system .usage of color filter makes algorithm more robust on LPL, too. The algorithm can efficiently determine and adjust the plate rotation in skewed images. It finds the optimal adaptive threshold corresponding to the intensity image obtained after adjusting the image intensity values. To segment the character of the license plate, a segmentation algorithm base on profile is proposed. An optical character recognition (OCR) engine has then been proposed. The OCR engine includes characters dilation, resizing input vector of ANN. To recognize the characters on the plates, MLP neural networks have been used and compared with Hopfield, LVQ and RBF. The results show that MLP outperforms. According to the results, the performance of the proposed system is better even in case of low-quality images or in images with illumination effects and noise
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.
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.
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Automatic License Plate Recognition Using Optical Character Recognition Based on Image
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Automatic License Plate Recognition Using
Optical Character Recognition Based on Image
Processing
Ms. Ankita Lad1
, Mr. Dhaval Patel2
1
Student, Department of Computer Science and Engineering, Hasmukh Goswami college of Engineering,
Ahmedabad, Gujarat, India
2
Assi. Prof., Department of Computer Science and Engineering, Hasmukh Goswami college of
Engineering, Ahmedabad, Gujarat, India
ABSTRACT
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.
Keyword: License plate recognition, character recognition, character segmentation, template matching, optical
character recognition.
1. Introduction
Automatic License plate recognition (ALPR) is a form of automatic vehicle identification. It is an image processing
technology used to identify vehicle by only their license plate [1]. Automatic License Plate Recognition (ALPR) is
one of the fundamental techniques of Intelligent Traffic System (ITS) [2], [3].
The ALPR was invented in 1976 at the Police Scientific Development branch in UK. However, it gained much
interest in last decade along with the improvement with the digital camera & the increase in computational activity,
it is simply the ability to automatically extract and recognize a vehicle number plate character from an image. In
essence it consist of a camera or frame grabber that has capability to grab an image, find the location of the number
in the image & extract the character for character recognition tool to translate the picture in to numerical readable
character [4]. It could be used to detect and prevent criminal activities and for security control of restricted area like
military zones or area around top government office.
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Fig. 1 ALPR System
The automatic license plate recognition system is developed in MATLAB. In this system first a vehicle image is
taken as input. Then the plate area is detected and extracted from image. After successfully extracting the number
plate character segmentation is performed. Finally the character recognition using template matching algorithm is
performed.
The paper organized as follows: Section 2 presents literature survey of license plate recognition system, Section 3
presents the proposed methodology for license plate recognition, Section 4 presents the experimental results, Section
5 shows result analysis and Section 6 presents conclusion.
2. Literature Survey
Chengpu Yu et al. [3] adopted the feature of characters’ stroke width and color match of license plate to locate and
segment the license plate. In the paper, author used color match feature and stroke width constraint to identify the
character edges in the license plate. As the English and digital letter has only one connected region, they use the
number of holes to represent the interior structure and peripheral profiles of the character to represent the exterior
feature.
Jitendra Sharma et al. [4] proposed a license plate recognition technique that uses Wavelet Transform for the
improvement of the recognition rate and time for character recognition. It is proposed a technique of Neural network
for vehicle license plate recognition. It has analyzed the performance of the system using Radical Basis Function
Network. The proposed methodology provides the better performance in comparison of correlation based method.
M. T. Qadri and M. Asif [5] implemented the Automatic Number Plate Recognition (ANPR) system on the entrance
of highly restricted area. In this paper, the ANPR system is implemented in MATLAB, and its performance is tasted
on real image.
K. Kaur and V. K. Banga [6] presented an algorithm for vehicle number identification using Optical Character
Recognition (OCR) technique. In this paper, the first step is capturing the image approximately 1 meter from the
number plate with camera. The purpose is to get a clear image without distortion. The second step is cropping the
number plate from captured image. The cropped image is the input for the character recognition. The third step is
character recognition. Then OCR technique is used to recognize an optically processed printed character number
plate which is based on template matching. The template matching affects the accuracy of the number plate.
Zhao et al. [7] presented an algorithm based on morphology and Least Square Support Vector Machine (LS-SVM)
in LPR system. It applies the improved Robert edge operator to detect the edge, dilates and erodes the edge image to
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locate the license plate. For segmentation, horizontal and vertical projection is used. Finally, construction of several
classifiers applying LS-SVM to carry out the character recognition process is performed.
Kapil Bhosale et al. [8] developed a number plate recognition system for toll collection. This paper mainly focuses
on Indian number plate. In this paper high resolution digital camera is used to acquire an image, preprocessing is
used to improve contrast of an image and reduce the noise in the image. For binarization of an image, a binary
method called Ostu’s metod is used. Adaptive thresholding filter has been used to process segmentation.
T. D. Duan et al. [9] presented the boundary line-based that optimizes speed and accuracy by combining the Hough
transform and Contour algorithm. The Contour algorithm is used to detect closed boundaries of objects. These
contour lines are transformed to Hough coordinate to find two interacted parallel lines that are considered as a plate-
candidate. Since there are quite few pixels in the contour lines, the transformation of these points to Hough
coordinate required much less computation. Therefore, the speed of the algorithm is improved without loss of
accuracy.
V. Koval et al. [10] describes the Smart Vehicle Screening System for automated recognition of vehicle license plate
information using a photograph of a vehicle. There are considered an approach to identify vehicle through
recognizing of its license plate using image fusion, neural networks and threshold techniques. The neural network
was used to successful recognition of license plate.
N. Vishwanath et al. [11] proposed a hybrid character segmentation algorithm that involves license plate
normalization and object enhancement technique as an image preprocessing step, followed by Hough transformation
based horizontal and vertical segmentation steps for Indian license plate character segmentation.
3. Proposed Methodology
Fig 2. Proposed system Flowchart
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The proposed system mainly works in three stages:
(1) License Plate Detection
(2) Character Segmentation
(3) Character Recognition
Stage 1: In this stage image acquisition and license plate detection are performed. For the first, a vehicle image is
taken as input. The image taken is in the RGB format. Then the image is converted to gray scale image. After
converting the RGB image to gray image, edge detection using Maxican Hat operator is perfomed. Then median
filtering is applied to remove unwanted noise.
Stage 2: In this stage license plate extraction and charcter segmentation is performed. For that morphological
dilation and erosion operations are performed to fill holes.
Morphological erosion operation can be defined as:
Morphological erosion operation can be defined as:
After applying morphological operations, local thresholding is applied to covert gray image into binary image. In
order to get further contrast enhancement, intensity range of the pixel values are scaled between 0 to 1. In certain
situations if some unwanted gaps and holes are present in the plate region. For that erosion operation is repeated for
removing such kind of holes or gaps. Then region growing segmentation is performed to segment characters from
the plate region.
Stage 3: In this stage character recognition is done using template matching. Each segmented character is matched
with character templates stored in database. Finally the string can be stored in a file to display number of vehicle.
4. Experimental Results
The system is simulated in MATLAB. The input image is taken of size 640*480 pixels. Images taken as input are in
RGB format. Fig. 3(a) shows input image. The converted gray image is shown in fig. 3(b). Fig. 3(c) shows the
output of edge detection. For detecting edges the Maxican Hat operator is used. Then noise is removed using median
filtering which is shown in fig. 3(d). Fig. 3(e) shows the result of morphological operations. The output of the
thresholding is shown in fig. 3(f). Fig. 3(g) shows the output of region growing segmentation. Fig. 3(h) shows
output of segmented characters. After recognizing number the number is stored in a file which is shown in fig. 3(i).
Fig. 3(a) Fig. 3(b) Fig. 3(c)
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Fig. 3(d) Fig. 3(e) Fig. 3(f)
Fig. 3(h) Fig. 3(h)
Fig. 3(i)
5. Result Analysis
The license plate recognition system was tested on MS Windows-7 Operating System. The software implementation
is done using MATLAB 7.10.0. The images are taken in RGB format. The resolution of image is 640 x 480 pixels.
The result shows that the developed system successfully detects the vehicle license plates when images are taken
from fixed distance or from the centre view. There are few problems where system fails to detect the plate region
when images are affected by luminance condition or problematic background. If the image is too dark in color then
system cannot detect the license plate region correctly.
When the license plate is successfully detected, it is easier to segment and recognize the characters of the license
plate. The system works with 98% accuracy when images taken from fixed distance or centre view. The system
improves the character recognition performance especially in dealing complex scenes like recognizing (0,O), (5,S),
(4,A). The recognition of characters is done using 10 digits and 26 alphabets.
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6. Conclusion
The developed proposed system uses series of image processing techniques to detect, segment and recognize the
vehicle license plate. The system is implemented in MATLAB. The system focuses on Indian standard number
plate. The main focus in developing this system is to improve the character recognition performance while dealing
with complex scenes like O,0,S,5,4,A. The system is tested with nearly 40 images. This system gives about 98% of
result in license plate recognition.
References
[1] Sandra sivanandan, Ashwini Dhanait, Yogita Dhapale and Yasmin Saiyyad , “Automatic Vehicle Indenification
Using License Plate Recognition for Indian Vehicles”, International Journal of Computer Applications, 2012.
[2] Roy, A.; Ghoshal ,D.P. ; “Number Plate Recognition for Use in Different Countries Using an Improved
Segmentation”, in 2nd
National Conference on Emerging Trends and Applications in Computer Science , IEEE,
2011, pp.1-5.
[3] Chengpu Yu, Mei Xie, Jin Qi, “A Novel System Design of License Plate Recognition”, International Symposium
on Computational Intelligence and Design , 2008.
[4] J. Sharma, A Mishra, K. Saxena and S. Kumar , “A Hybrid Technique for License Plate Recognition Based on
Feature Selection of Wavelet Transform and Artificial Neural Network”, International Conference on
Reliability, Optimization and Information Technology, 2014.
[5] Muhammad Tahir Qadri, Muhammad Asif, “Automatic Number Plate Recognition System for Vehicle
Identification Using Optical Character Recognition”, International Conference on Education Technology and
Computer, 2009.
[6] Er. Kavneet Kaur, Vijay Kumar Banga, “Number Plate Recognition Using OCR Technique”, International
Journal of Research in Engineering and Technology, vol 2, Issue. 09, sep-2013.
[7] H.Zhao; C. Song; H. Zhao; S. Zhang, “License Plate Recognition System based on Morphology and LS-SVM”,
IEEE International Conference, pp. 826-829, August 2008.
[8] Kapil Bhosale, Jigdish Jadav, Sumit Kalyankar, R. R. Bhambare, “Number Plate Recognition System for Toll
Collection”, International Journal of Emerging Technology and Advanced Engineering , vol. 4, Issue. 4, April
2014.
[9] T. D. Duan, D. A. Duc, T. L. Du; “Combining Hough Transform and Contour Algorithm for Detecting Vehicles’
License-Plates”, International Symposium on Intelligent, Multimedia, Video and Speech Processing, October
2004.
[10] V. Koval, V. Turchenko, V. Kochan, A. Sachenko and G. Markowsky, “Smart License Plate Recognition
System Based on Image Processing Using Neural Network”, IEEE International Workshop on Intelligent Data
Acquisition and Advanced Computing System, September 2003.
[11] P. Ramasubramanian, R. Jerlin Emiliya, R. Janaki, B. Gifston Daniel and C. Anand, “Number Plate
Recognition and Character Segmentation using Eight-Neighbors and Hybrid Binarization Techniques”,
International Conference on Communication and Signal processing, April 2014.