This document discusses license plate character recognition using the back propagation algorithm. It begins with an overview of license plate recognition (LPR) technology and its importance in intelligent transportation systems. The LPR system consists of three main phases: 1) license plate extraction, 2) character segmentation, and 3) character recognition. It then describes each phase in more detail. License plate extraction locates the license plate region in an image. Character segmentation isolates individual characters from the license plate region. Finally, character recognition identifies the characters to read the license plate. The document proposes using a back propagation neural network for character recognition and summarizes its three major stages of license plate localization, character segmentation, and character recognition. It concludes that neural networks and back
The document describes AspenAir commercial HVAC air filters and their benefits, including 10-20% HVAC energy savings from reduced fan energy consumption and equipment run-time. The filters have superior air quality with a 99% capture rate down to 0.1 microns and 99% bacterial kill rate. They also provide 50-75% filter maintenance savings from reduced frequency of filter changes, saving on labor and material costs. The filters have a 1-2 year payback period and 10 year expected product life.
Certificado de participación congreso hortenciaprofesoraudp
El certificado confirma que la profesora María Hortencia Soto Rojas participó en un congreso regional de profesores de 8 horas sobre formar comunidades profesionales de aprendizaje reflexivas que relacionan el aprendizaje, enseñanza e investigación basados en un enfoque pedagógico indagatorio para la educación en ciencias, el cual se llevó a cabo el 22 de abril de 2016 en la Facultad de Medicina Occidente de la Universidad de Chile.
Juan Jose Jorda Aguilar is a highly experienced international field service technician with over 25 years of experience in gas compression service and maintenance. He has extensive knowledge of high speed reciprocating compressors, slow speed integral compressors, and commissioning of new equipment. Jorda Aguilar has worked in Venezuela, Spain, Nigeria, Netherlands, Tunisia, England, and Gabon, specializing in operation, maintenance, and troubleshooting of Waukesha and Caterpillar engines. He is proficient in Spanish, English, French, and Italian and has various safety and technical training qualifications.
Life of a promotional product game board apparel edition tc (1)Jenny Norby
The facts are the facts Sometimes I think we choose NOT to give a t-shirt/pen/bag because we think well everyone. In reality everyone does it because they work.
Our CyberPatriot team from the Montgomery Squadron placed first in Virginia in the Gold level of the All Service Division for CyberPatriot VIII. While we did not place in the top Platinum level that determines the official State winners, our score exceeded the second place score in the Platinum level. Additionally, we ranked 4th in the nation for CAP in the Gold level All Service Division and 16th overall in the nation for that category, placing 263rd across all levels and divisions.
Movie Nights in the Heights will show the Pixar film "Inside Out" on Friday, June 10th at 8:30 p.m. next to the Nature Trails off Viesca Street. This event is part of the summer Movie Nights in the Heights series and is sponsored by an unspecified organization.
Taha Ali Hassan al-Mashhari is a married man from Saudi Arabia seeking a job opportunity. He has over 10 years of experience in human resources, collections, and brand management. He holds several IT certifications and degrees in computer science, English, and database development. His skills include proficiency with software companies, Microsoft Windows, teamwork, communication, and self-motivation.
The document describes AspenAir commercial HVAC air filters and their benefits, including 10-20% HVAC energy savings from reduced fan energy consumption and equipment run-time. The filters have superior air quality with a 99% capture rate down to 0.1 microns and 99% bacterial kill rate. They also provide 50-75% filter maintenance savings from reduced frequency of filter changes, saving on labor and material costs. The filters have a 1-2 year payback period and 10 year expected product life.
Certificado de participación congreso hortenciaprofesoraudp
El certificado confirma que la profesora María Hortencia Soto Rojas participó en un congreso regional de profesores de 8 horas sobre formar comunidades profesionales de aprendizaje reflexivas que relacionan el aprendizaje, enseñanza e investigación basados en un enfoque pedagógico indagatorio para la educación en ciencias, el cual se llevó a cabo el 22 de abril de 2016 en la Facultad de Medicina Occidente de la Universidad de Chile.
Juan Jose Jorda Aguilar is a highly experienced international field service technician with over 25 years of experience in gas compression service and maintenance. He has extensive knowledge of high speed reciprocating compressors, slow speed integral compressors, and commissioning of new equipment. Jorda Aguilar has worked in Venezuela, Spain, Nigeria, Netherlands, Tunisia, England, and Gabon, specializing in operation, maintenance, and troubleshooting of Waukesha and Caterpillar engines. He is proficient in Spanish, English, French, and Italian and has various safety and technical training qualifications.
Life of a promotional product game board apparel edition tc (1)Jenny Norby
The facts are the facts Sometimes I think we choose NOT to give a t-shirt/pen/bag because we think well everyone. In reality everyone does it because they work.
Our CyberPatriot team from the Montgomery Squadron placed first in Virginia in the Gold level of the All Service Division for CyberPatriot VIII. While we did not place in the top Platinum level that determines the official State winners, our score exceeded the second place score in the Platinum level. Additionally, we ranked 4th in the nation for CAP in the Gold level All Service Division and 16th overall in the nation for that category, placing 263rd across all levels and divisions.
Movie Nights in the Heights will show the Pixar film "Inside Out" on Friday, June 10th at 8:30 p.m. next to the Nature Trails off Viesca Street. This event is part of the summer Movie Nights in the Heights series and is sponsored by an unspecified organization.
Taha Ali Hassan al-Mashhari is a married man from Saudi Arabia seeking a job opportunity. He has over 10 years of experience in human resources, collections, and brand management. He holds several IT certifications and degrees in computer science, English, and database development. His skills include proficiency with software companies, Microsoft Windows, teamwork, communication, and self-motivation.
This document summarizes and compares different techniques for license plate recognition, including artificial neural networks, radial basis function neural networks, and template matching. It reviews past research on using these techniques for license plate detection, character segmentation, and character recognition. The document also provides an introduction to license plate recognition systems and their components - plate localization, preprocessing, segmentation, normalization, and optical character recognition.
This document summarizes and compares different techniques for license plate recognition, including artificial neural networks, radial basis function neural networks, and template matching. It reviews past research on using these techniques for license plate detection, character segmentation, and character recognition. The document also analyzes the pros and cons of license plate recognition methods that use RBF neural networks and template matching.
Comparative Study of Different Techniques for License Plate RecognitionEditor Jacotech
This document summarizes and compares different techniques for license plate recognition, including artificial neural networks, radial basis function neural networks, and template matching. It first presents an abstract discussing license plate detection and recognition applications and challenges. It then reviews literature on license plate detection using radial basis function neural networks, discussing three papers that used text-line construction, preprocessing, and a radial basis function neural network approach. The document aims to provide an enhanced and comprehensive view of research in license plate detection and recognition techniques.
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.
IRJET- iSecurity: The AI Surveillance, a Smart Tracking SystemIRJET Journal
The document describes an AI surveillance system called iSecurity that aims to help police catch criminals more easily. The system uses CCTV footage to capture license plate numbers of vehicles, store their locations in a database, and display recent locations of a specific vehicle on a map. This avoids manually searching hours of footage. The system has three parts: video processing to obtain license plate numbers using techniques like OpenCV, storing license plate and location data in a database, and displaying recent vehicle locations on a map using APIs. It is developed in Python for its machine learning and image processing libraries.
This document describes a license plate recognition system developed by students at CEC, Karnataka, India. It begins with an abstract that outlines license plate recognition and the key steps: capturing images, pre-processing, segmentation, and character recognition using Python. The introduction provides more details on license plate recognition systems and their uses. A literature review summarizes 10 relevant papers on license plate recognition techniques. The proposed system is then described, outlining the steps of image acquisition, desaturation, thresholding, morphological operations, segmentation, and character recognition.
License Plate Recognition System for Moving Vehicles Using Laplacian Edge De...IRJET Journal
This document presents a license plate recognition system for moving vehicles that uses the Laplacian edge detector and feature extraction. The system first applies Otsu's method to binarize the captured vehicle image and reduce noise using a median filter. It then uses the Laplacian operator to detect edges and locate the license plate region. The characters in the plate are segmented and normalized. Finally, features are extracted from the characters for recognition, allowing the system to handle characters written in English or Hindi scripts. The proposed approach aims to address challenges in recognizing Indian license plates, which can vary in format and contain text in multiple languages.
Automated License Plate Recognition for Toll Booth ApplicationIJERA Editor
This document describes an automated license plate recognition system that can be used for toll booth applications. It analyzes images of vehicles to detect and recognize their license plates for automated payment of tolls. The system uses image processing techniques like thresholding, edge detection and template matching to locate and extract the license plate from an image. It then recognizes the characters on the plate using a neural network and compares it to databases to obtain vehicle information. The key steps involve preprocessing the image, segmenting the license plate region, extracting and enhancing the characters, and recognizing the plate number. This automated system allows contactless and real-time processing of vehicles for toll payment and has applications in traffic management and security control.
This document describes a license plate recognition system that uses a convolutional neural network to identify vehicle license plates from images. The system consists of four main steps: 1) plate localization to extract the license plate region from images, 2) preprocessing using a homomorphic filter to enhance images, 3) segmentation to separate characters, and 4) using a CNN model to recognize individual characters and output the full license plate number. The system was tested on images with mostly accurate character recognition. Potential applications are monitoring vehicle entries at societies and helping with traffic management and stolen vehicle identification.
IRJET - Indian Vehicle License Plate Recognition for Vehicle and Owner Identi...IRJET Journal
This document describes a proposed system for Indian vehicle license plate recognition and using the plate number to retrieve vehicle and owner details from a public database. The system uses computer vision techniques like edge detection and optical character recognition to first detect the license plate in an image and extract the plate number as text. It then sends requests to the Indian government's vehicle database website to retrieve and display details about the vehicle and registered owner like name, registration date, vehicle model, insurance, and other information. The goal is to help with tasks like traffic management, parking enforcement, and verifying vehicle registration details. The proposed approach involves license plate detection, OCR, sending database requests, and displaying the results.
The document summarizes a proposed license plate recognition system for Indian vehicles. The system works in three modules: license plate localization, character segmentation, and character recognition. License plate localization is performed using morphological operations and edge detection. Character segmentation uses connected component labeling. Character recognition employs a neural network classifier. The system was tested on 100 Indian vehicle images, achieving 86% accuracy for localization, 81% for segmentation, and 80% for character recognition. The overall goal of the system is to automatically recognize license plate numbers from vehicle images captured in India.
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).
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.
The document describes an automatic license plate recognition system (LPRS) that consists of three main modules: license plate detection, character segmentation, and optical character recognition (OCR). The license plate detection module uses preprocessing, morphological operations, and horizontal/vertical segmentation to identify license plate regions. Character segmentation converts images to grayscale, performs binarization, and further segments images horizontally and vertically. The OCR module is trained on character templates then uses template matching to recognize characters by comparing pixel values between segmented characters and stored templates. The system has applications in traffic monitoring, electronic toll collection, surveillance, and safety systems.
IRJET- Vehicle Number Plate Recognition SystemIRJET Journal
This document summarizes a research paper on a vehicle number plate recognition system. The system uses image processing techniques like preprocessing, segmentation, and optical character recognition to extract characters from vehicle number plates in images. The key steps are preprocessing the image to remove noise, segmenting the number plate from the vehicle image, isolating individual characters, and recognizing the characters using a template matching algorithm. The system is intended to help with applications like traffic enforcement, toll collection, and vehicle surveillance and management.
IRJET- Recognition of Indian License Plate Number from Live Stream VideosIRJET Journal
This document presents a survey of existing automatic number plate recognition (ANPR) systems and proposes a new approach using k-nearest neighbors (k-NN), OpenALPR, and convolutional neural networks (CNNs). It first discusses challenges with license plate recognition in India due to regional fonts. Existing ANPR systems are reviewed along with their drawbacks. The proposed method involves detecting license plates in images and videos, extracting characters, and recognizing plates using k-NN, OpenALPR and a CNN model. Results of the three approaches on test data will be analyzed to determine the most accurate one, considering factors like character recognition accuracy and processing time.
Iaetsd fpga implementation of various security based tollgate system using anprIaetsd Iaetsd
The document describes an automatic toll collection system using license plate recognition (LPR) technology. The system uses cameras to capture images of vehicles' license plates as they pass through the toll gate. An image processing algorithm segments the license plate from the image and recognizes the characters. The recognized license plate number is matched to a database to retrieve payment and vehicle ownership details. Based on the vehicle weight detected by a pressure sensor and the database lookup, the appropriate toll is calculated and deducted. Sensors also detect any hazardous gases in vehicles and trigger an alarm and gate closure. The system is designed and implemented using a field programmable gate array (FPGA) for high-speed operation and testing on real-world scenarios.
An automatic license plate recognition system (LPRS) uses computer vision techniques to detect, recognize, and identify vehicle license plates. It consists of three main modules: license plate detection using image preprocessing and morphological operations, character segmentation through binarization and segmentation, and optical character recognition using template matching. LPRS has applications in traffic monitoring, electronic toll collection, and surveillance. Previous work has investigated various methods for each stage, such as edge statistics for plate localization and projection/clustering for character segmentation.
IRJET- Advenced Traffic Management System using Automatic Number Plate Recogn...IRJET Journal
This document describes an advanced traffic management system using automatic number plate recognition (ANPR). It discusses how current vehicle tracking systems have limitations in identifying fake number plates. The proposed system uses image processing and computer vision techniques to identify the number plate and type of vehicle from images or video. It extracts the number plate using edge detection and morphological operations. Optical character recognition and template matching are then used to recognize the characters. A convolutional neural network classifies the vehicle type. The system can check if a number plate is fake by comparing with an RTO database, and alert police if a match is found to a wanted vehicle number. It aims to help police track vehicles of interest and improve traffic management.
Indonesian license plate recognition with improved horizontal-vertical edge p...nooriasukmaningtyas
License plate recognition (LPR) is one of the classical problems in the
field of object recognition. Its application is very crucial in the
automation of transportation system since it helps to recognise a vehicle
identity, which information is stored in the license plate. LPR usually
consists of three major phases: pre-processing, license plate localisation,
optical character recognition (OCR). Despite being classical, its
implementation faced with much more complicated problems in the real
scenario. This paper proposed an improved LPR algorithm based on
modified horizontal-vertical edge projection. The method uses for
detecting and localising the region of interest. It is done using the
horizontal and vertical projection of the image. Related works proved
that the modified horizontal-vertical edge projection is the simplest
method to be implemented, yet very effective against Indonesian license
plate. However, its performance gets reduced when specular reflection
occurs in the sample image. Therefore, morphological operations are
utilised in the pre-processing phase to reduce such effects while
preserving the needed information. Eighty sample images which
captured using various camera configurations were used in this research.
Based on the experimental results, our proposed algorithm shows an
improvement compared with the previous study and successfully detect
71 license plates in 80 image samples which results in 88.75% accuracy.
Electrically small antennas: The art of miniaturizationEditor IJARCET
We are living in the technological era, were we preferred to have the portable devices rather than unmovable devices. We are isolating our self rom the wires and we are becoming the habitual of wireless world what makes the device portable? I guess physical dimensions (mechanical) of that particular device, but along with this the electrical dimension is of the device is also of great importance. Reducing the physical dimension of the antenna would result in the small antenna but not electrically small antenna. We have different definition for the electrically small antenna but the one which is most appropriate is, where k is the wave number and is equal to and a is the radius of the imaginary sphere circumscribing the maximum dimension of the antenna. As the present day electronic devices progress to diminish in size, technocrats have become increasingly concentrated on electrically small antenna (ESA) designs to reduce the size of the antenna in the overall electronics system. Researchers in many fields, including RF and Microwave, biomedical technology and national intelligence, can benefit from electrically small antennas as long as the performance of the designed ESA meets the system requirement.
This document provides a comparative study of two-way finite automata and Turing machines. Some key points:
- Two-way finite automata are similar to read-only Turing machines in that they have a finite tape that can be read in both directions, but cannot write to the tape.
- Turing machines have an infinite tape that can be read from and written to, allowing them to recognize recursively enumerable languages.
- Both models are examined in their ability to accept the regular language L={anbm|m,n>0}.
- The time complexity of a two-way finite automaton for this language is O(n2) due to making two passes over the
This document summarizes and compares different techniques for license plate recognition, including artificial neural networks, radial basis function neural networks, and template matching. It reviews past research on using these techniques for license plate detection, character segmentation, and character recognition. The document also provides an introduction to license plate recognition systems and their components - plate localization, preprocessing, segmentation, normalization, and optical character recognition.
This document summarizes and compares different techniques for license plate recognition, including artificial neural networks, radial basis function neural networks, and template matching. It reviews past research on using these techniques for license plate detection, character segmentation, and character recognition. The document also analyzes the pros and cons of license plate recognition methods that use RBF neural networks and template matching.
Comparative Study of Different Techniques for License Plate RecognitionEditor Jacotech
This document summarizes and compares different techniques for license plate recognition, including artificial neural networks, radial basis function neural networks, and template matching. It first presents an abstract discussing license plate detection and recognition applications and challenges. It then reviews literature on license plate detection using radial basis function neural networks, discussing three papers that used text-line construction, preprocessing, and a radial basis function neural network approach. The document aims to provide an enhanced and comprehensive view of research in license plate detection and recognition techniques.
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.
IRJET- iSecurity: The AI Surveillance, a Smart Tracking SystemIRJET Journal
The document describes an AI surveillance system called iSecurity that aims to help police catch criminals more easily. The system uses CCTV footage to capture license plate numbers of vehicles, store their locations in a database, and display recent locations of a specific vehicle on a map. This avoids manually searching hours of footage. The system has three parts: video processing to obtain license plate numbers using techniques like OpenCV, storing license plate and location data in a database, and displaying recent vehicle locations on a map using APIs. It is developed in Python for its machine learning and image processing libraries.
This document describes a license plate recognition system developed by students at CEC, Karnataka, India. It begins with an abstract that outlines license plate recognition and the key steps: capturing images, pre-processing, segmentation, and character recognition using Python. The introduction provides more details on license plate recognition systems and their uses. A literature review summarizes 10 relevant papers on license plate recognition techniques. The proposed system is then described, outlining the steps of image acquisition, desaturation, thresholding, morphological operations, segmentation, and character recognition.
License Plate Recognition System for Moving Vehicles Using Laplacian Edge De...IRJET Journal
This document presents a license plate recognition system for moving vehicles that uses the Laplacian edge detector and feature extraction. The system first applies Otsu's method to binarize the captured vehicle image and reduce noise using a median filter. It then uses the Laplacian operator to detect edges and locate the license plate region. The characters in the plate are segmented and normalized. Finally, features are extracted from the characters for recognition, allowing the system to handle characters written in English or Hindi scripts. The proposed approach aims to address challenges in recognizing Indian license plates, which can vary in format and contain text in multiple languages.
Automated License Plate Recognition for Toll Booth ApplicationIJERA Editor
This document describes an automated license plate recognition system that can be used for toll booth applications. It analyzes images of vehicles to detect and recognize their license plates for automated payment of tolls. The system uses image processing techniques like thresholding, edge detection and template matching to locate and extract the license plate from an image. It then recognizes the characters on the plate using a neural network and compares it to databases to obtain vehicle information. The key steps involve preprocessing the image, segmenting the license plate region, extracting and enhancing the characters, and recognizing the plate number. This automated system allows contactless and real-time processing of vehicles for toll payment and has applications in traffic management and security control.
This document describes a license plate recognition system that uses a convolutional neural network to identify vehicle license plates from images. The system consists of four main steps: 1) plate localization to extract the license plate region from images, 2) preprocessing using a homomorphic filter to enhance images, 3) segmentation to separate characters, and 4) using a CNN model to recognize individual characters and output the full license plate number. The system was tested on images with mostly accurate character recognition. Potential applications are monitoring vehicle entries at societies and helping with traffic management and stolen vehicle identification.
IRJET - Indian Vehicle License Plate Recognition for Vehicle and Owner Identi...IRJET Journal
This document describes a proposed system for Indian vehicle license plate recognition and using the plate number to retrieve vehicle and owner details from a public database. The system uses computer vision techniques like edge detection and optical character recognition to first detect the license plate in an image and extract the plate number as text. It then sends requests to the Indian government's vehicle database website to retrieve and display details about the vehicle and registered owner like name, registration date, vehicle model, insurance, and other information. The goal is to help with tasks like traffic management, parking enforcement, and verifying vehicle registration details. The proposed approach involves license plate detection, OCR, sending database requests, and displaying the results.
The document summarizes a proposed license plate recognition system for Indian vehicles. The system works in three modules: license plate localization, character segmentation, and character recognition. License plate localization is performed using morphological operations and edge detection. Character segmentation uses connected component labeling. Character recognition employs a neural network classifier. The system was tested on 100 Indian vehicle images, achieving 86% accuracy for localization, 81% for segmentation, and 80% for character recognition. The overall goal of the system is to automatically recognize license plate numbers from vehicle images captured in India.
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).
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.
The document describes an automatic license plate recognition system (LPRS) that consists of three main modules: license plate detection, character segmentation, and optical character recognition (OCR). The license plate detection module uses preprocessing, morphological operations, and horizontal/vertical segmentation to identify license plate regions. Character segmentation converts images to grayscale, performs binarization, and further segments images horizontally and vertically. The OCR module is trained on character templates then uses template matching to recognize characters by comparing pixel values between segmented characters and stored templates. The system has applications in traffic monitoring, electronic toll collection, surveillance, and safety systems.
IRJET- Vehicle Number Plate Recognition SystemIRJET Journal
This document summarizes a research paper on a vehicle number plate recognition system. The system uses image processing techniques like preprocessing, segmentation, and optical character recognition to extract characters from vehicle number plates in images. The key steps are preprocessing the image to remove noise, segmenting the number plate from the vehicle image, isolating individual characters, and recognizing the characters using a template matching algorithm. The system is intended to help with applications like traffic enforcement, toll collection, and vehicle surveillance and management.
IRJET- Recognition of Indian License Plate Number from Live Stream VideosIRJET Journal
This document presents a survey of existing automatic number plate recognition (ANPR) systems and proposes a new approach using k-nearest neighbors (k-NN), OpenALPR, and convolutional neural networks (CNNs). It first discusses challenges with license plate recognition in India due to regional fonts. Existing ANPR systems are reviewed along with their drawbacks. The proposed method involves detecting license plates in images and videos, extracting characters, and recognizing plates using k-NN, OpenALPR and a CNN model. Results of the three approaches on test data will be analyzed to determine the most accurate one, considering factors like character recognition accuracy and processing time.
Iaetsd fpga implementation of various security based tollgate system using anprIaetsd Iaetsd
The document describes an automatic toll collection system using license plate recognition (LPR) technology. The system uses cameras to capture images of vehicles' license plates as they pass through the toll gate. An image processing algorithm segments the license plate from the image and recognizes the characters. The recognized license plate number is matched to a database to retrieve payment and vehicle ownership details. Based on the vehicle weight detected by a pressure sensor and the database lookup, the appropriate toll is calculated and deducted. Sensors also detect any hazardous gases in vehicles and trigger an alarm and gate closure. The system is designed and implemented using a field programmable gate array (FPGA) for high-speed operation and testing on real-world scenarios.
An automatic license plate recognition system (LPRS) uses computer vision techniques to detect, recognize, and identify vehicle license plates. It consists of three main modules: license plate detection using image preprocessing and morphological operations, character segmentation through binarization and segmentation, and optical character recognition using template matching. LPRS has applications in traffic monitoring, electronic toll collection, and surveillance. Previous work has investigated various methods for each stage, such as edge statistics for plate localization and projection/clustering for character segmentation.
IRJET- Advenced Traffic Management System using Automatic Number Plate Recogn...IRJET Journal
This document describes an advanced traffic management system using automatic number plate recognition (ANPR). It discusses how current vehicle tracking systems have limitations in identifying fake number plates. The proposed system uses image processing and computer vision techniques to identify the number plate and type of vehicle from images or video. It extracts the number plate using edge detection and morphological operations. Optical character recognition and template matching are then used to recognize the characters. A convolutional neural network classifies the vehicle type. The system can check if a number plate is fake by comparing with an RTO database, and alert police if a match is found to a wanted vehicle number. It aims to help police track vehicles of interest and improve traffic management.
Indonesian license plate recognition with improved horizontal-vertical edge p...nooriasukmaningtyas
License plate recognition (LPR) is one of the classical problems in the
field of object recognition. Its application is very crucial in the
automation of transportation system since it helps to recognise a vehicle
identity, which information is stored in the license plate. LPR usually
consists of three major phases: pre-processing, license plate localisation,
optical character recognition (OCR). Despite being classical, its
implementation faced with much more complicated problems in the real
scenario. This paper proposed an improved LPR algorithm based on
modified horizontal-vertical edge projection. The method uses for
detecting and localising the region of interest. It is done using the
horizontal and vertical projection of the image. Related works proved
that the modified horizontal-vertical edge projection is the simplest
method to be implemented, yet very effective against Indonesian license
plate. However, its performance gets reduced when specular reflection
occurs in the sample image. Therefore, morphological operations are
utilised in the pre-processing phase to reduce such effects while
preserving the needed information. Eighty sample images which
captured using various camera configurations were used in this research.
Based on the experimental results, our proposed algorithm shows an
improvement compared with the previous study and successfully detect
71 license plates in 80 image samples which results in 88.75% accuracy.
Electrically small antennas: The art of miniaturizationEditor IJARCET
We are living in the technological era, were we preferred to have the portable devices rather than unmovable devices. We are isolating our self rom the wires and we are becoming the habitual of wireless world what makes the device portable? I guess physical dimensions (mechanical) of that particular device, but along with this the electrical dimension is of the device is also of great importance. Reducing the physical dimension of the antenna would result in the small antenna but not electrically small antenna. We have different definition for the electrically small antenna but the one which is most appropriate is, where k is the wave number and is equal to and a is the radius of the imaginary sphere circumscribing the maximum dimension of the antenna. As the present day electronic devices progress to diminish in size, technocrats have become increasingly concentrated on electrically small antenna (ESA) designs to reduce the size of the antenna in the overall electronics system. Researchers in many fields, including RF and Microwave, biomedical technology and national intelligence, can benefit from electrically small antennas as long as the performance of the designed ESA meets the system requirement.
This document provides a comparative study of two-way finite automata and Turing machines. Some key points:
- Two-way finite automata are similar to read-only Turing machines in that they have a finite tape that can be read in both directions, but cannot write to the tape.
- Turing machines have an infinite tape that can be read from and written to, allowing them to recognize recursively enumerable languages.
- Both models are examined in their ability to accept the regular language L={anbm|m,n>0}.
- The time complexity of a two-way finite automaton for this language is O(n2) due to making two passes over the
This document analyzes and compares the performance of the AODV and DSDV routing protocols in a vehicular ad hoc network (VANET) simulation. Simulations were conducted using NS-2, SUMO, and MOVE simulators for a grid map scenario with varying numbers of nodes. The results show that AODV performed better than DSDV in terms of throughput and packet delivery fraction, while DSDV had lower end-to-end delays. However, neither protocol was found to be fully suitable for the highly dynamic VANET environment. The document concludes that further work is needed to develop improved routing protocols optimized for VANETs.
This document discusses the digital circuit layout problem and approaches to solving it using graph partitioning techniques. It begins by introducing the digital circuit layout problem and how it has become more complex with increasing circuit sizes. It then discusses how the problem can be decomposed into subproblems using graph partitioning to assign geometric coordinates to circuit components. The document reviews several traditional approaches to solve the problem, such as the Kernighan-Lin algorithm, and discusses their limitations for larger circuit sizes. It also discusses more recent approaches using evolutionary algorithms and concludes by analyzing the contributions of various approaches.
This document summarizes various data mining techniques that have been used for intrusion detection systems. It first describes the architecture of a data mining-based IDS, including sensors to collect data, detectors to evaluate the data using detection models, a data warehouse for storage, and a model generator. It then discusses supervised and unsupervised learning approaches that have been applied, including neural networks, support vector machines, K-means clustering, and self-organizing maps. Finally, it reviews several related works applying these techniques and compares their results, finding that combinations of approaches can improve detection rates while reducing false alarms.
This document provides an overview of speech recognition systems and recent progress in the field. It discusses different types of speech recognition including isolated word, connected word, continuous speech, and spontaneous speech. Various techniques used in speech recognition are also summarized, such as simulated evolutionary computation, artificial neural networks, fuzzy logic, Kalman filters, and Hidden Markov Models. The document reviews several papers published between 2004-2012 that studied speech recognition methods including using dynamic spectral subband centroids, Kalman filters, biomimetic computing techniques, noise estimation, and modulation filtering. It concludes that Hidden Markov Models combined with MFCC features provide good recognition results for large vocabulary, speaker-independent, continuous speech recognition.
This document discusses integrating two assembly lines, Line A and Line B, based on lean line design concepts to reduce space and operators. It analyzes the current state of the lines using tools like takt time analysis and MTM/UAS studies. Improvements are identified to eliminate waste, including methods improvements, workplace rearrangement, ergonomic changes, and outsourcing. Paper kaizen is conducted and work elements are retimed. The goal is to integrate the lines to better utilize space and manpower while meeting manufacturing standards.
This document summarizes research on the exposure of microwaves from cellular networks. It describes how microwaves interact with biological systems and discusses measurement techniques and safety standards regarding microwave exposure. While some studies have alleged health hazards from microwaves, independent reviews by health organizations have found no evidence that exposure to microwaves below international safety limits causes harm. The document concludes that with precautions like limiting exposure time and using phones with lower SAR ratings, microwaves from cell phones pose minimal health risks.
This document summarizes a research paper that examines the effect of feature reduction in sentiment analysis of online reviews. It uses principle component analysis to reduce the number of features (product attributes) from a dataset of 500 camera reviews labeled as positive or negative. Two models are developed - one using the original set of 95 product attributes, and one using the reduced set. Support vector machines and naive Bayes classifiers are applied to both models and their performance is evaluated to determine if classification accuracy can be maintained while using fewer features. The results show it is possible to achieve similar accuracy levels with less features, improving computational efficiency.
This document provides a review of multispectral palm image fusion techniques. It begins with an introduction to biometrics and palm print identification. Different palm print images capture different spectral information about the palm. The document then reviews several pixel-level fusion methods for combining multispectral palm images, finding that Curvelet transform performs best at preserving discriminative patterns. It also discusses hardware for capturing multispectral palm images and the process of region of interest extraction and localization. Common fusion methods like wavelet transform and Curvelet transform are also summarized.
This document describes a vehicle theft detection system that uses radio frequency identification (RFID) technology. The system involves embedding an RFID chip in each vehicle that continuously transmits a unique identification signal. When a vehicle is stolen, the owner reports it to the police, who upload the vehicle's information to a central database. Police vehicles are equipped with RFID receivers. If a stolen vehicle passes within range of a receiver, the receiver detects the vehicle's ID signal and displays its details on a tablet. This allows police to quickly identify and recover stolen vehicles. The system aims to make it difficult for thieves to hide a vehicle's identity and allows vehicles to be tracked globally wherever the detection system is implemented.
This document discusses and compares two techniques for image denoising using wavelet transforms: Dual-Tree Complex DWT and Double-Density Dual-Tree Complex DWT. Both techniques decompose an image corrupted by noise using filter banks, apply thresholding to the wavelet coefficients, and reconstruct the image. The Double-Density Dual-Tree Complex DWT yields better denoising results than the Dual-Tree Complex DWT as it produces more directional wavelets and is less sensitive to shifts and noise variance. Experimental results on test images demonstrate that the Double-Density method achieves higher peak signal-to-noise ratios, especially at higher noise levels.
This document compares the k-means and grid density clustering algorithms. It summarizes that grid density clustering determines dense grids based on the densities of neighboring grids, and is able to handle different shaped clusters in multi-density environments. The grid density algorithm does not require distance computation and is not dependent on the number of clusters being known in advance like k-means. The document concludes that grid density clustering is better than k-means clustering as it can handle noise and outliers, find arbitrary shaped clusters, and has lower time complexity.
This document proposes a method for detecting, localizing, and extracting text from videos with complex backgrounds. It involves three main steps:
1. Text detection uses corner metric and Laplacian filtering techniques independently to detect text regions. Corner metric identifies regions with high curvature, while Laplacian filtering highlights intensity discontinuities. The results are combined through multiplication to reduce noise.
2. Text localization then determines the accurate boundaries of detected text strings.
3. Text binarization filters background pixels to extract text pixels for recognition. Thresholding techniques are used to convert localized text regions to binary images.
The method exploits different text properties to detect text using corner metric and Laplacian filtering. Combining the results improves
This document describes the design and implementation of a low power 16-bit arithmetic logic unit (ALU) using clock gating techniques. A variable block length carry skip adder is used in the arithmetic unit to reduce power consumption and improve performance. The ALU uses a clock gating circuit to selectively clock only the active arithmetic or logic unit, reducing dynamic power dissipation from unnecessary clock charging/discharging. The ALU was simulated in VHDL and synthesized for a Xilinx Spartan 3E FPGA, achieving a maximum frequency of 65.19MHz at 1.98mW power dissipation, demonstrating improved performance over a conventional ALU design.
This document describes using particle swarm optimization (PSO) and genetic algorithms (GA) to tune the parameters of a proportional-integral-derivative (PID) controller for an automatic voltage regulator (AVR) system. PSO and GA are used to minimize the objective function by adjusting the PID parameters to achieve optimal step response with minimal overshoot, settling time, and rise time. The results show that PSO provides high-quality solutions within a shorter calculation time than other stochastic methods.
This document discusses implementing trust negotiations in multisession transactions. It proposes a framework that supports voluntary and unexpected interruptions, allowing negotiating parties to complete negotiations despite temporary unavailability of resources. The Trust-x protocol addresses issues related to validity, temporary loss of data, and extended unavailability of one negotiator. It allows a peer to suspend an ongoing negotiation and resume it with another authenticated peer. Negotiation portions and intermediate states can be safely and privately passed among peers to guarantee stability for continued suspended negotiations. An ontology is also proposed to provide formal specification of concepts and relationships, which is essential in complex web service environments for sharing credential information needed to establish trust.
This document discusses and compares various nature-inspired optimization algorithms for resolving the mixed pixel problem in remote sensing imagery, including Biogeography-Based Optimization (BBO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). It provides an overview of each algorithm, explaining key concepts like migration and mutation in BBO. The document aims to prove that BBO is the best algorithm for resolving the mixed pixel problem by comparing it to other evolutionary algorithms. It also includes figures illustrating concepts like the species model and habitat in BBO.
This document discusses principal component analysis (PCA) for face recognition. It begins with an introduction to face recognition and PCA. PCA works by calculating eigenvectors from a set of face images, which represent the principal components that account for the most variance in the image data. These eigenvectors are called "eigenfaces" and can be used to reconstruct the face images. The document then discusses how the system is implemented, including preparing a face database, normalizing the training images, calculating the eigenfaces/principal components, projecting the face images into this reduced space, and recognizing faces by calculating distances between projected test images and training images.
This document summarizes research on using wireless sensor networks to detect mobile targets. It discusses two optimization problems: 1) maximizing the exposure of the least exposed path within a sensor budget, and 2) minimizing sensor installation costs while ensuring all paths have exposure above a threshold. It proposes using tabu search heuristics to provide near-optimal solutions. The research also addresses extending the models to consider wireless connectivity, heterogeneous sensors, and intrusion detection using a game theory approach. Experimental results show the proposed mobile replica detection scheme can rapidly detect replicas with no false positives or negatives.