The document presents a method for localizing and recognizing vehicle number plates from images. It involves three main stages:
1. Localization of the number plate using binarization combining global and local thresholding, followed by connected component analysis to filter noise.
2. Character segmentation using projection profiles and an approximation algorithm to separate characters from the plate.
3. Character recognition using SVM to classify segmented characters into letter and number classes.
The method achieves 97.1% accuracy for localization, 95.4% for segmentation, and 95.72% for recognition when tested on 560 images.
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%.
Vehicle License Plate Recognition (VLPR) is an important system for harmonious traffic. Moreover this system is helpful in many fields and places as private and public entrances, parking lots, border control and theft control. This paper presents a new framework for Sudanese VLPR system. The proposed framework uses Multi Objective Particle Swarm Optimization (MOPSO) and Connected Component Analysis (CCA) to extract the license plate. Horizontal and vertical projection will be used for character segmentation and the final recognition stage is based on the Artificial Immune System (AIS). A new dataset that contains samples for the current shape of Sudanese license plates will be used for training and testing the proposes framework.
Classification and Detection of Vehicles using Deep Learningijtsrd
The vehicle classification and detecting its license plate are important tasks in intelligent security and transportation systems. The traditional methods of vehicle classification and detection are highly complex which provides coarse grained results due to suffering from limited viewpoints. Because of the latest achievements of Deep Learning, it was successfully applied to image classification and detection of objects. This paper presents a method based on a convolutional neural network, which consists of two steps vehicle classification and vehicle license plate recognition. Several typical neural network modules have been applied in training and testing the vehicle Classification and detection of license plate model, such as CNN convolutional neural networks , TensorFlow, Tesseract OCR. The proposed method can identify the vehicle type, number plate and other information accurately. This model provides security and log details regarding vehicles by using AI Surveillance. It guides the surveillance operators and assists human resources. With the help of the original training dataset and enriched testing dataset, the algorithm can obtain results with an average accuracy of about 97.32 in the classification and detection of vehicles. By increasing the amount of the data, the mean error and misclassification rate gradually decreases. So, this algorithm which is based on Deep Learning has good superiority and adaptability. When compared to the leading methods in the challenging Image datasets, our deep learning approach obtains highly competitive results. Finally, this paper proposes modern methods for the improvement of the algorithm and prospects the development direction of deep learning in the field of machine learning and artificial intelligence. Madde Pavan Kumar | Dr. K. Manivel | N. Jayanthi "Classification & Detection of Vehicles using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30353.pdf Paper Url :https://www.ijtsrd.com/engineering/software-engineering/30353/classification-and-detection-of-vehicles-using-deep-learning/madde-pavan-kumar
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
VEHICLE CLASSIFICATION USING THE CONVOLUTION NEURAL NETWORK APPROACHJANAK TRIVEDI
We present vehicle detection classification using the Convolution
Neural Network (CNN) of the deep learning approach. The automatic vehicle
classification for traffic surveillance video systems is challenging for the Intelligent
Transportation System (ITS) to build a smart city. In this article, three different
vehicles: bike, car and truck classification are considered for around 3,000 bikes,
6,000 cars, and 2,000 images of trucks. CNN can automatically absorb and extract
different vehicle dataset’s different features without a manual selection of features.
The accuracy of CNN is measured in terms of the confidence values of the detected
object. The highest confidence value is about 0.99 in the case of the bike category
vehicle classification. The automatic vehicle classification supports building an
electronic toll collection system and identifying emergency vehicles in the traffic
An Analysis of Various Deep Learning Algorithms for Image Processingvivatechijri
Various applications of image processing has given it a wider scope when it comes to data analysis.
Various Machine Learning Algorithms provide a powerful environment for training modules effectively to
identify various entities of images and segment the same accordingly. Rather one can observe that though the
image classifiers like the Support Vector Machines (SVM) or Random Forest Algorithms do justice to the task,
deep learning algorithms like the Artificial Neural Networks (ANN) and its subordinates, the very well-known
and extremely powerful Algorithm Convolution Neural Networks (CNN) can provide a new dimension to the
image processing domain. It has way higher accuracy and computational power for classifying images further
and segregating their various entities as individual components of the image working region. Major focus will
be on the Region Convolution Neural Networks (R-CNN) algorithm and how well it provides the pixel-level
segmentation further using its better successors like the Fast-Faster and Mask R-CNN versions.
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%.
Vehicle License Plate Recognition (VLPR) is an important system for harmonious traffic. Moreover this system is helpful in many fields and places as private and public entrances, parking lots, border control and theft control. This paper presents a new framework for Sudanese VLPR system. The proposed framework uses Multi Objective Particle Swarm Optimization (MOPSO) and Connected Component Analysis (CCA) to extract the license plate. Horizontal and vertical projection will be used for character segmentation and the final recognition stage is based on the Artificial Immune System (AIS). A new dataset that contains samples for the current shape of Sudanese license plates will be used for training and testing the proposes framework.
Classification and Detection of Vehicles using Deep Learningijtsrd
The vehicle classification and detecting its license plate are important tasks in intelligent security and transportation systems. The traditional methods of vehicle classification and detection are highly complex which provides coarse grained results due to suffering from limited viewpoints. Because of the latest achievements of Deep Learning, it was successfully applied to image classification and detection of objects. This paper presents a method based on a convolutional neural network, which consists of two steps vehicle classification and vehicle license plate recognition. Several typical neural network modules have been applied in training and testing the vehicle Classification and detection of license plate model, such as CNN convolutional neural networks , TensorFlow, Tesseract OCR. The proposed method can identify the vehicle type, number plate and other information accurately. This model provides security and log details regarding vehicles by using AI Surveillance. It guides the surveillance operators and assists human resources. With the help of the original training dataset and enriched testing dataset, the algorithm can obtain results with an average accuracy of about 97.32 in the classification and detection of vehicles. By increasing the amount of the data, the mean error and misclassification rate gradually decreases. So, this algorithm which is based on Deep Learning has good superiority and adaptability. When compared to the leading methods in the challenging Image datasets, our deep learning approach obtains highly competitive results. Finally, this paper proposes modern methods for the improvement of the algorithm and prospects the development direction of deep learning in the field of machine learning and artificial intelligence. Madde Pavan Kumar | Dr. K. Manivel | N. Jayanthi "Classification & Detection of Vehicles using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30353.pdf Paper Url :https://www.ijtsrd.com/engineering/software-engineering/30353/classification-and-detection-of-vehicles-using-deep-learning/madde-pavan-kumar
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
VEHICLE CLASSIFICATION USING THE CONVOLUTION NEURAL NETWORK APPROACHJANAK TRIVEDI
We present vehicle detection classification using the Convolution
Neural Network (CNN) of the deep learning approach. The automatic vehicle
classification for traffic surveillance video systems is challenging for the Intelligent
Transportation System (ITS) to build a smart city. In this article, three different
vehicles: bike, car and truck classification are considered for around 3,000 bikes,
6,000 cars, and 2,000 images of trucks. CNN can automatically absorb and extract
different vehicle dataset’s different features without a manual selection of features.
The accuracy of CNN is measured in terms of the confidence values of the detected
object. The highest confidence value is about 0.99 in the case of the bike category
vehicle classification. The automatic vehicle classification supports building an
electronic toll collection system and identifying emergency vehicles in the traffic
An Analysis of Various Deep Learning Algorithms for Image Processingvivatechijri
Various applications of image processing has given it a wider scope when it comes to data analysis.
Various Machine Learning Algorithms provide a powerful environment for training modules effectively to
identify various entities of images and segment the same accordingly. Rather one can observe that though the
image classifiers like the Support Vector Machines (SVM) or Random Forest Algorithms do justice to the task,
deep learning algorithms like the Artificial Neural Networks (ANN) and its subordinates, the very well-known
and extremely powerful Algorithm Convolution Neural Networks (CNN) can provide a new dimension to the
image processing domain. It has way higher accuracy and computational power for classifying images further
and segregating their various entities as individual components of the image working region. Major focus will
be on the Region Convolution Neural Networks (R-CNN) algorithm and how well it provides the pixel-level
segmentation further using its better successors like the Fast-Faster and Mask R-CNN versions.
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.
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.
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).
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.
CANNY EDGE DETECTION BASED REAL-TIME INTELLIGENT PARKING MANAGEMENT SYSTEMJANAK TRIVEDI
Real-time traffic monitoring and parking are very important aspects
for a better social and economic system. Python-based Intelligent Parking
Management System (IPMS) module using a USB camera and a canny edge
detection method was developed. The current situation of real-time parking slot
was simultaneously checked, both online and via a mobile application, with a
message of Parking “Available” or “Not available” for 10 parking slots. In
addition, at the time entering in parking module, gate open and at the time of exit
parking module, the gate closes automatically using servomotor and sensors.
Results are displayed in figures with the proposed method flow chart
Data entry is a time consuming and erroneous procedure in its nature. In addition, validity
check of submitted information is not easier than retyping it. In a mega-corporation like Kanoon
Farhangi Amoozesh, there are almost no way to control the authenticity of students' educational
background. By the virtue of fast computer architectures, optical character recognition, a.k.a.
OCR, systems have become viable. Unfortunately, general-purpose OCR systems like Google's
Tesseract are not handful because they don't have any a-priori information about what they are
reading. In this paper the authors have taken a in-depth look on what has done in the field of
OCR in the last 60 years. Then, a custom-made system adapted to the problem is presented
which is way more accurate than general purpose OCRs. The developed system reads more than
60 digits per second. As shown in the Results section, the accuracy of the devised method is
reasonable enough to be exposed in public use.
OpenCVand Matlab based Car Parking System Module for Smart City using Circle ...JANAK TRIVEDI
finding parking availability for a specific time period is
a very tedious job in urban areas. The Indian government now
focusing on t he smart city project, already they published city
name for a n upcoming smart city project. In smart city
application , intelligent transportation system (ITS) plays an
important role- in that finding parking place, specifically for the
car owner to avoid time computation, as well as congestion in
traffic is going to be very important. In this article, we propose
an intelligent car parking system for the smart city using Circle
Hough Transform (CHT).
"Detecting road lane is one of the key processes in vision based driving assistance system and autonomous vehicle system. The main purpose of the lane detection process is to estimate car position relative to the lane so that it can provide a warning to the driver if the car starts departing the lane. This process is useful not only to enhance safe driving but also in self driving car system. A novel approach to lane detection method using image processing techniques is presented in this research. The method minimizes the complexity of computation by the use of prior knowledge of color, intensity and the shape of the lane marks. By using prior knowledge, the detection process requires only two different analyses which are pixel intensity analysis and color component analysis. The method starts with searching a strong pair of edges along the horizontal line of road image. Once the strong edge is detected the process continues with color analysis on pixels that lie between the edges to check whether the pixels belong to a lane or not. The process is repeated for different positions of horizontal lines covering the road image. The method was successfully tested on selected 20 road images collected from internet. Ery M. Rizaldy | J. M. Nursherida | Abdul Rahim Sadiq Batcha ""Reduced Dimension Lane Detection Method"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advanced Engineering and Information Technology , November 2018, URL: https://www.ijtsrd.com/papers/ijtsrd19136.pdf
Paper URL: https://www.ijtsrd.com/engineering/civil-engineering/19136/reduced-dimension-lane-detection-method/ery-m-rizaldy"
Automatic face and VLP’S recognition for smart parking systemTELKOMNIKA JOURNAL
One of the concerning issues regarding smart city is Smart Parking. In Smart Parking, some
researchers try to provide solutions and breakthroughs on several research topics among security
systems, the availability of single space, an IoT framework, etc. In this study, we proposed a security
system on Smart Parking based on face recognition and VLP’s (Vehicle License Plates) identification. In
this research, SSIM (Structural Similarity) method as part of IQA has been applied due to its reliability and
simple computation for face detection and recognition process. From the test results of 30 data, obtained
the highest SSIM value 0.83 with the highest accuracy rate of 76.67%. That level of accuracy still has not
reached the implementation standard of 99.9%. So that it still needs to be improved in the future studies,
especially in the filtering noise section.
A Novel Framework For Numerical Character Recognition With Zoning Distance Fe...IJERD Editor
Advancements of Computer technology has made every organization to implement the automatic processing systems for its activities. One of the examples is the recognition of handwritten characters, which has always been a challenging task in image processing and pattern recognition. In this paper we propose Zone based features for recognition of the handwritten characters. In this zoning approach a digit image is divided into 8x8 zones and centre pixel is computed for each zone. This procedure is sequentially repeated for entire zone. Finally features are extracted for classification and recognition.
Real-time parking slot availability for Bhavnagar, using statistical block ma...JANAK TRIVEDI
Purpose-The purpose of this paper is to find a real-time parking location for a four-wheeler. Design/methodology/approach-Real-time parking availability using specific infrastructure requires a high cost of installation and maintenance cost, which is not affordable to all urban cities. The authors present statistical block matching algorithm (SBMA) for real-time parking management in small-town cities such as Bhavnagar using an in-built surveillance CCTV system, which is not installed for parking application. In particular, data from a camera situated in a mall was used to detect the parking status of some specific parking places using a region of interest (ROI). The method proposed computes the mean value of the pixels inside the ROI using blocks of different sizes (8 Â 10 and 20 Â 35), and the values were compared among different frames. When the difference between frames is more significant than a threshold, the process generates "no parking space for that place." Otherwise, the method yields "parking place available." Then, this information is used to print a bounding box on the parking places with the color green/red to show the availability of the parking place. Findings-The real-time feedback loop (car parking positions) helps the presented model and dynamically refines the parking strategy and parking position to the users. A whole-day experiment/validation is shown in this paper, where the evaluation of the method is performed using pattern recognition metrics for classification: precision, recall and F1 score. Originality/value-The authors found real-time parking availability for Himalaya Mall situated in Bhavnagar, Gujarat, for 18th June 2018 video using the SBMA method with accountable computational time for finding parking slots. The limitations of the presented method with future implementation are discussed at the end of this paper.
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.
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.
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).
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.
CANNY EDGE DETECTION BASED REAL-TIME INTELLIGENT PARKING MANAGEMENT SYSTEMJANAK TRIVEDI
Real-time traffic monitoring and parking are very important aspects
for a better social and economic system. Python-based Intelligent Parking
Management System (IPMS) module using a USB camera and a canny edge
detection method was developed. The current situation of real-time parking slot
was simultaneously checked, both online and via a mobile application, with a
message of Parking “Available” or “Not available” for 10 parking slots. In
addition, at the time entering in parking module, gate open and at the time of exit
parking module, the gate closes automatically using servomotor and sensors.
Results are displayed in figures with the proposed method flow chart
Data entry is a time consuming and erroneous procedure in its nature. In addition, validity
check of submitted information is not easier than retyping it. In a mega-corporation like Kanoon
Farhangi Amoozesh, there are almost no way to control the authenticity of students' educational
background. By the virtue of fast computer architectures, optical character recognition, a.k.a.
OCR, systems have become viable. Unfortunately, general-purpose OCR systems like Google's
Tesseract are not handful because they don't have any a-priori information about what they are
reading. In this paper the authors have taken a in-depth look on what has done in the field of
OCR in the last 60 years. Then, a custom-made system adapted to the problem is presented
which is way more accurate than general purpose OCRs. The developed system reads more than
60 digits per second. As shown in the Results section, the accuracy of the devised method is
reasonable enough to be exposed in public use.
OpenCVand Matlab based Car Parking System Module for Smart City using Circle ...JANAK TRIVEDI
finding parking availability for a specific time period is
a very tedious job in urban areas. The Indian government now
focusing on t he smart city project, already they published city
name for a n upcoming smart city project. In smart city
application , intelligent transportation system (ITS) plays an
important role- in that finding parking place, specifically for the
car owner to avoid time computation, as well as congestion in
traffic is going to be very important. In this article, we propose
an intelligent car parking system for the smart city using Circle
Hough Transform (CHT).
"Detecting road lane is one of the key processes in vision based driving assistance system and autonomous vehicle system. The main purpose of the lane detection process is to estimate car position relative to the lane so that it can provide a warning to the driver if the car starts departing the lane. This process is useful not only to enhance safe driving but also in self driving car system. A novel approach to lane detection method using image processing techniques is presented in this research. The method minimizes the complexity of computation by the use of prior knowledge of color, intensity and the shape of the lane marks. By using prior knowledge, the detection process requires only two different analyses which are pixel intensity analysis and color component analysis. The method starts with searching a strong pair of edges along the horizontal line of road image. Once the strong edge is detected the process continues with color analysis on pixels that lie between the edges to check whether the pixels belong to a lane or not. The process is repeated for different positions of horizontal lines covering the road image. The method was successfully tested on selected 20 road images collected from internet. Ery M. Rizaldy | J. M. Nursherida | Abdul Rahim Sadiq Batcha ""Reduced Dimension Lane Detection Method"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advanced Engineering and Information Technology , November 2018, URL: https://www.ijtsrd.com/papers/ijtsrd19136.pdf
Paper URL: https://www.ijtsrd.com/engineering/civil-engineering/19136/reduced-dimension-lane-detection-method/ery-m-rizaldy"
Automatic face and VLP’S recognition for smart parking systemTELKOMNIKA JOURNAL
One of the concerning issues regarding smart city is Smart Parking. In Smart Parking, some
researchers try to provide solutions and breakthroughs on several research topics among security
systems, the availability of single space, an IoT framework, etc. In this study, we proposed a security
system on Smart Parking based on face recognition and VLP’s (Vehicle License Plates) identification. In
this research, SSIM (Structural Similarity) method as part of IQA has been applied due to its reliability and
simple computation for face detection and recognition process. From the test results of 30 data, obtained
the highest SSIM value 0.83 with the highest accuracy rate of 76.67%. That level of accuracy still has not
reached the implementation standard of 99.9%. So that it still needs to be improved in the future studies,
especially in the filtering noise section.
A Novel Framework For Numerical Character Recognition With Zoning Distance Fe...IJERD Editor
Advancements of Computer technology has made every organization to implement the automatic processing systems for its activities. One of the examples is the recognition of handwritten characters, which has always been a challenging task in image processing and pattern recognition. In this paper we propose Zone based features for recognition of the handwritten characters. In this zoning approach a digit image is divided into 8x8 zones and centre pixel is computed for each zone. This procedure is sequentially repeated for entire zone. Finally features are extracted for classification and recognition.
Real-time parking slot availability for Bhavnagar, using statistical block ma...JANAK TRIVEDI
Purpose-The purpose of this paper is to find a real-time parking location for a four-wheeler. Design/methodology/approach-Real-time parking availability using specific infrastructure requires a high cost of installation and maintenance cost, which is not affordable to all urban cities. The authors present statistical block matching algorithm (SBMA) for real-time parking management in small-town cities such as Bhavnagar using an in-built surveillance CCTV system, which is not installed for parking application. In particular, data from a camera situated in a mall was used to detect the parking status of some specific parking places using a region of interest (ROI). The method proposed computes the mean value of the pixels inside the ROI using blocks of different sizes (8 Â 10 and 20 Â 35), and the values were compared among different frames. When the difference between frames is more significant than a threshold, the process generates "no parking space for that place." Otherwise, the method yields "parking place available." Then, this information is used to print a bounding box on the parking places with the color green/red to show the availability of the parking place. Findings-The real-time feedback loop (car parking positions) helps the presented model and dynamically refines the parking strategy and parking position to the users. A whole-day experiment/validation is shown in this paper, where the evaluation of the method is performed using pattern recognition metrics for classification: precision, recall and F1 score. Originality/value-The authors found real-time parking availability for Himalaya Mall situated in Bhavnagar, Gujarat, for 18th June 2018 video using the SBMA method with accountable computational time for finding parking slots. The limitations of the presented method with future implementation are discussed at the end of this paper.
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.
Automatic licence plate recognition (LPR) has been a subject of study for the last few decades. Considering the recent advancements in machine learning methods and portable devices, this increasingly attracting researchers’ interest to provide more reliable LPR systems. Several LPR techniques have been reported in the literature in different intelligent transportation applications and surveillance systems, and yet a ropust LPR system remains a challenging research task. Because the performance of current techniques is subject to several factors and local conditions, this paper aims to explore the use of LPR in a specific application; i.e. Automatic plate recognition to monitor the entry and exit of vehicles at the university campus gates. Implementing an auto-gate system is an important application for a smooth control of flowing traffic especially during peak hours. We propose an automated system with the ability to capture, verify and recognize the license plates using image processing-based techniques. The system is aimed to work alongside existing access cards and other gate remote controls. Experimental evaluation of the system reveals a detection accuracy of 91.58%, a successful plate number segmentation rate of 91% and 80% accuracy of plate recognition.
Character recognition of kannada text in scene images using neuralIAEME Publication
Character recognition in scene images is one of the most fascinating and challenging
areas of pattern recognition with various practical application potentials. It can contribute
immensely to the advancement of an automation process and can improve the interface
between man and machine in many applications. Some practical application potentials of
character recognition system are: reading aid for the blind, traffic guidance systems, tour
guide systems, location aware systems and many more. In this work, a novel method for
recognizing basic Kannada characters in natural scene images is proposed. The proposed
method uses zone wise horizontal and vertical profile based features of character images. The
method works in two phases. During training, zone wise vertical and horizontal profile based
features are extracted from training samples and neural network is trained. During testing, the
test image is processed to obtain features and recognized using neural network classifier. The
method has been evaluated on 490 Kannada character images captured from 2 Mega Pixels
cameras on mobile phones at various sizes 240x320, 600x800 and 900x1200, which contains
samples of different sizes, styles and with different degradations, and achieves an average
recognition accuracy of 92%. The system is efficient and insensitive to the variations in size
and font, noise, blur and other degradations.
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.
Learning to Recognize Distance to Stop Signs Using the Virtual World of Grand...Artur Filipowicz
This paper examines the use of a convolutional neural network and a virtual environment to detect stop signs and estimate distances to them based on individual images. To train the network, we develop a method to automatically collect labeled data from Grand Theft Auto 5, a video game. Using this method, we collect a dataset of 1.4 million images with and without stop signs across different environments, weather conditions, and times of day. Convolutional neural network trained and tested on this data can detect 95.5% of the stops signs within 20 meters of the vehicle with a false positive rate of 5.6% and an average error in distance of 1.2m to 2.4m on video game data. We also discovered that the performance our approach is limited in distance to about 20m. The applicability of these results to real world driving is tested, appears promising and must be studied further.
Statistics indicate that most road accidents occur due to a lack of time to react to instant traffic. This problem can be addressed with self-driving vehicles with the application of automated systems to detect such traffic events. The Autonomous Vehicle Navigation System (ATS) has been a standard in the Intelligent Transport System (ITS) and many Driver Assistance Systems (DAS) have been adopted to support these Advanced Autonomous Vehicles (IAVs). To develop these recognition systems for automated self-driving cars, it's important to monitor and operate in real-time traffic events. It requires the correct detection and response of traffic event an automated vehicle. In this paper proposed to develop such a system by applying image recognition to detect and respond to a road blocker by means of real-time distance measurement. To study the performance by measuring accuracy and precision of road blocker detection system and distance calculation, various experiments were conducted by using Shalom frame dataset and detection accuracy, precision of 99%, 100%, while distance calculation 97%, 99% has been achieved by this approach.
A Method for Sudanese Vehicle License Plates Detection and ExtractionEditor IJCATR
License Plate Detection and Extraction is an important phase of Vehicle License Plate Recognition systems, which has been
an active research topic in the computer vision domain in order to identify vehicles by their license plates without direct human
intervention. This paper presents a simple, fast and automatic License Plate Detection method for the current shape of Sudanese license
plate. The proposed method involves several steps: green channel extraction, edge detection, regions of interest selection, dilation
operation with especial structural element and connected component analysis. In order to analyze the performance and efficiency of the
proposed method a data set for Sudanese vehicles has been created. Using this new data set, number of experiments has been carried
out. Comparing with other countries license plate detection the achieved results is satisfactory.
Traffic sign detection optimization using color and shape segmentation as pre...TELKOMNIKA JOURNAL
One of performance indicator of an Autonomous Vehicle (AV) is its ability to accomodate rapid environment changing; and performance of traffic sign detection (TSD) system is one of them. A low frame rate of TSD impacts to late decision making and may cause to a fatal accident. Meanwhile, adding any GPU to TSD will significantly increases its cost and make it unaffordable. This paper proposed a pre-processing system for TSD which implement a color and a shape segmentation to increase the system speed. These segmentation systems filter input frames such that the number of frames sent to AI system is reduced. As a result, workload of AI system is decreased and its frame rate increases. HSV threshold is used in color segmentation to filter frames with no desired color. This algorithm ignores the saturation when performing color detection. Further, an edge detection feature is employed in shape segmentation to count the total contours of an object. Using German Traffic Sign Recognition Benchmark dataset as model, the pre-processing system filters 97% of frames with no traffic sign objects and has an accuracy of 88%. TSD system proposed allows a frame rate improvement up to 32 FPS when YOLO algorithm is used.
Similar to PROJECTION PROFILE BASED NUMBER PLATE LOCALIZATION AND RECOGNITION (20)
ANALYSIS OF LAND SURFACE DEFORMATION GRADIENT BY DINSAR cscpconf
The progressive development of Synthetic Aperture Radar (SAR) systems diversify the exploitation of the generated images by these systems in different applications of geoscience. Detection and monitoring surface deformations, procreated by various phenomena had benefited from this evolution and had been realized by interferometry (InSAR) and differential interferometry (DInSAR) techniques. Nevertheless, spatial and temporal decorrelations of the interferometric couples used, limit strongly the precision of analysis results by these techniques. In this context, we propose, in this work, a methodological approach of surface deformation detection and analysis by differential interferograms to show the limits of this technique according to noise quality and level. The detectability model is generated from the deformation signatures, by simulating a linear fault merged to the images couples of ERS1 / ERS2 sensors acquired in a region of the Algerian south.
4D AUTOMATIC LIP-READING FOR SPEAKER'S FACE IDENTIFCATIONcscpconf
A novel based a trajectory-guided, concatenating approach for synthesizing high-quality image real sample renders video is proposed . The lips reading automated is seeking for modeled the closest real image sample sequence preserve in the library under the data video to the HMM predicted trajectory. The object trajectory is modeled obtained by projecting the face patterns into an KDA feature space is estimated. The approach for speaker's face identification by using synthesise the identity surface of a subject face from a small sample of patterns which sparsely each the view sphere. An KDA algorithm use to the Lip-reading image is discrimination, after that work consisted of in the low dimensional for the fundamental lip features vector is reduced by using the 2D-DCT.The mouth of the set area dimensionality is ordered by a normally reduction base on the PCA to obtain the Eigen lips approach, their proposed approach by[33]. The subjective performance results of the cost function under the automatic lips reading modeled , which wasn’t illustrate the superior performance of the
method.
MOVING FROM WATERFALL TO AGILE PROCESS IN SOFTWARE ENGINEERING CAPSTONE PROJE...cscpconf
Universities offer software engineering capstone course to simulate a real world-working environment in which students can work in a team for a fixed period to deliver a quality product. The objective of the paper is to report on our experience in moving from Waterfall process to Agile process in conducting the software engineering capstone project. We present the capstone course designs for both Waterfall driven and Agile driven methodologies that highlight the structure, deliverables and assessment plans.To evaluate the improvement, we conducted a survey for two different sections taught by two different instructors to evaluate students’ experience in moving from traditional Waterfall model to Agile like process. Twentyeight students filled the survey. The survey consisted of eight multiple-choice questions and an open-ended question to collect feedback from students. The survey results show that students were able to attain hands one experience, which simulate a real world-working environment. The results also show that the Agile approach helped students to have overall better design and avoid mistakes they have made in the initial design completed in of the first phase of the capstone project. In addition, they were able to decide on their team capabilities, training needs and thus learn the required technologies earlier which is reflected on the final product quality
PROMOTING STUDENT ENGAGEMENT USING SOCIAL MEDIA TECHNOLOGIEScscpconf
Using social media in education provides learners with an informal way for communication. Informal communication tends to remove barriers and hence promotes student engagement. This paper presents our experience in using three different social media technologies in teaching software project management course. We conducted different surveys at the end of every semester to evaluate students’ satisfaction and engagement. Results show that using social media enhances students’ engagement and satisfaction. However, familiarity with the tool is an important factor for student satisfaction.
A SURVEY ON QUESTION ANSWERING SYSTEMS: THE ADVANCES OF FUZZY LOGICcscpconf
In real world computing environment with using a computer to answer questions has been a human dream since the beginning of the digital era, Question-answering systems are referred to as intelligent systems, that can be used to provide responses for the questions being asked by the user based on certain facts or rules stored in the knowledge base it can generate answers of questions asked in natural , and the first main idea of fuzzy logic was to working on the problem of computer understanding of natural language, so this survey paper provides an overview on what Question-Answering is and its system architecture and the possible relationship and
different with fuzzy logic, as well as the previous related research with respect to approaches that were followed. At the end, the survey provides an analytical discussion of the proposed QA models, along or combined with fuzzy logic and their main contributions and limitations.
DYNAMIC PHONE WARPING – A METHOD TO MEASURE THE DISTANCE BETWEEN PRONUNCIATIONS cscpconf
Human beings generate different speech waveforms while speaking the same word at different times. Also, different human beings have different accents and generate significantly varying speech waveforms for the same word. There is a need to measure the distances between various words which facilitate preparation of pronunciation dictionaries. A new algorithm called Dynamic Phone Warping (DPW) is presented in this paper. It uses dynamic programming technique for global alignment and shortest distance measurements. The DPW algorithm can be used to enhance the pronunciation dictionaries of the well-known languages like English or to build pronunciation dictionaries to the less known sparse languages. The precision measurement experiments show 88.9% accuracy.
INTELLIGENT ELECTRONIC ASSESSMENT FOR SUBJECTIVE EXAMS cscpconf
In education, the use of electronic (E) examination systems is not a novel idea, as Eexamination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity. The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
TWO DISCRETE BINARY VERSIONS OF AFRICAN BUFFALO OPTIMIZATION METAHEURISTICcscpconf
African Buffalo Optimization (ABO) is one of the most recent swarms intelligence based metaheuristics. ABO algorithm is inspired by the buffalo’s behavior and lifestyle. Unfortunately, the standard ABO algorithm is proposed only for continuous optimization problems. In this paper, the authors propose two discrete binary ABO algorithms to deal with binary optimization problems. In the first version (called SBABO) they use the sigmoid function and probability model to generate binary solutions. In the second version (called LBABO) they use some logical operator to operate the binary solutions. Computational results on two knapsack problems (KP and MKP) instances show the effectiveness of the proposed algorithm and their ability to achieve good and promising solutions.
DETECTION OF ALGORITHMICALLY GENERATED MALICIOUS DOMAINcscpconf
In recent years, many malware writers have relied on Dynamic Domain Name Services (DDNS) to maintain their Command and Control (C&C) network infrastructure to ensure a persistence presence on a compromised host. Amongst the various DDNS techniques, Domain Generation Algorithm (DGA) is often perceived as the most difficult to detect using traditional methods. This paper presents an approach for detecting DGA using frequency analysis of the character distribution and the weighted scores of the domain names. The approach’s feasibility is demonstrated using a range of legitimate domains and a number of malicious algorithmicallygenerated domain names. Findings from this study show that domain names made up of English characters “a-z” achieving a weighted score of < 45 are often associated with DGA. When a weighted score of < 45 is applied to the Alexa one million list of domain names, only 15% of the domain names were treated as non-human generated.
GLOBAL MUSIC ASSET ASSURANCE DIGITAL CURRENCY: A DRM SOLUTION FOR STREAMING C...cscpconf
The amount of piracy in the streaming digital content in general and the music industry in specific is posing a real challenge to digital content owners. This paper presents a DRM solution to monetizing, tracking and controlling online streaming content cross platforms for IP enabled devices. The paper benefits from the current advances in Blockchain and cryptocurrencies. Specifically, the paper presents a Global Music Asset Assurance (GoMAA) digital currency and presents the iMediaStreams Blockchain to enable the secure dissemination and tracking of the streamed content. The proposed solution provides the data owner the ability to control the flow of information even after it has been released by creating a secure, selfinstalled, cross platform reader located on the digital content file header. The proposed system provides the content owners’ options to manage their digital information (audio, video, speech, etc.), including the tracking of the most consumed segments, once it is release. The system benefits from token distribution between the content owner (Music Bands), the content distributer (Online Radio Stations) and the content consumer(Fans) on the system blockchain.
IMPORTANCE OF VERB SUFFIX MAPPING IN DISCOURSE TRANSLATION SYSTEMcscpconf
This paper discusses the importance of verb suffix mapping in Discourse translation system. In
discourse translation, the crucial step is Anaphora resolution and generation. In Anaphora
resolution, cohesion links like pronouns are identified between portions of text. These binders
make the text cohesive by referring to nouns appearing in the previous sentences or nouns
appearing in sentences after them. In Machine Translation systems, to convert the source
language sentences into meaningful target language sentences the verb suffixes should be
changed as per the cohesion links identified. This step of translation process is emphasized in
the present paper. Specifically, the discussion is on how the verbs change according to the
subjects and anaphors. To explain the concept, English is used as the source language (SL) and
an Indian language Telugu is used as Target language (TL)
EXACT SOLUTIONS OF A FAMILY OF HIGHER-DIMENSIONAL SPACE-TIME FRACTIONAL KDV-T...cscpconf
In this paper, based on the definition of conformable fractional derivative, the functional
variable method (FVM) is proposed to seek the exact traveling wave solutions of two higherdimensional
space-time fractional KdV-type equations in mathematical physics, namely the
(3+1)-dimensional space–time fractional Zakharov-Kuznetsov (ZK) equation and the (2+1)-
dimensional space–time fractional Generalized Zakharov-Kuznetsov-Benjamin-Bona-Mahony
(GZK-BBM) equation. Some new solutions are procured and depicted. These solutions, which
contain kink-shaped, singular kink, bell-shaped soliton, singular soliton and periodic wave
solutions, have many potential applications in mathematical physics and engineering. The
simplicity and reliability of the proposed method is verified.
AUTOMATED PENETRATION TESTING: AN OVERVIEWcscpconf
The using of information technology resources is rapidly increasing in organizations,
businesses, and even governments, that led to arise various attacks, and vulnerabilities in the
field. All resources make it a must to do frequently a penetration test (PT) for the environment
and see what can the attacker gain and what is the current environment's vulnerabilities. This
paper reviews some of the automated penetration testing techniques and presents its
enhancement over the traditional manual approaches. To the best of our knowledge, it is the
first research that takes into consideration the concept of penetration testing and the standards
in the area.This research tackles the comparison between the manual and automated
penetration testing, the main tools used in penetration testing. Additionally, compares between
some methodologies used to build an automated penetration testing platform.
CLASSIFICATION OF ALZHEIMER USING fMRI DATA AND BRAIN NETWORKcscpconf
Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing
attention of neuroscientists and computer scientists, since it opens a new window to explore
functional network of human brain with relatively high resolution. BOLD technique provides
almost accurate state of brain. Past researches prove that neuro diseases damage the brain
network interaction, protein- protein interaction and gene-gene interaction. A number of
neurological research paper also analyse the relationship among damaged part. By
computational method especially machine learning technique we can show such classifications.
In this paper we used OASIS fMRI dataset affected with Alzheimer’s disease and normal
patient’s dataset. After proper processing the fMRI data we use the processed data to form
classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve
Bayes. We also compare the accuracy of our proposed method with existing methods. In future,
we will other combinations of methods for better accuracy.
VALIDATION METHOD OF FUZZY ASSOCIATION RULES BASED ON FUZZY FORMAL CONCEPT AN...cscpconf
In order to treat and analyze real datasets, fuzzy association rules have been proposed. Several
algorithms have been introduced to extract these rules. However, these algorithms suffer from
the problems of utility, redundancy and large number of extracted fuzzy association rules. The
expert will then be confronted with this huge amount of fuzzy association rules. The task of
validation becomes fastidious. In order to solve these problems, we propose a new validation
method. Our method is based on three steps. (i) We extract a generic base of non redundant
fuzzy association rules by applying EFAR-PN algorithm based on fuzzy formal concept analysis.
(ii) we categorize extracted rules into groups and (iii) we evaluate the relevance of these rules
using structural equation model.
PROBABILITY BASED CLUSTER EXPANSION OVERSAMPLING TECHNIQUE FOR IMBALANCED DATAcscpconf
In many applications of data mining, class imbalance is noticed when examples in one class are
overrepresented. Traditional classifiers result in poor accuracy of the minority class due to the
class imbalance. Further, the presence of within class imbalance where classes are composed of
multiple sub-concepts with different number of examples also affect the performance of
classifier. In this paper, we propose an oversampling technique that handles between class and
within class imbalance simultaneously and also takes into consideration the generalization
ability in data space. The proposed method is based on two steps- performing Model Based
Clustering with respect to classes to identify the sub-concepts; and then computing the
separating hyperplane based on equal posterior probability between the classes. The proposed
method is tested on 10 publicly available data sets and the result shows that the proposed
method is statistically superior to other existing oversampling methods.
CHARACTER AND IMAGE RECOGNITION FOR DATA CATALOGING IN ECOLOGICAL RESEARCHcscpconf
Data collection is an essential, but manpower intensive procedure in ecological research. An
algorithm was developed by the author which incorporated two important computer vision
techniques to automate data cataloging for butterfly measurements. Optical Character
Recognition is used for character recognition and Contour Detection is used for imageprocessing.
Proper pre-processing is first done on the images to improve accuracy. Although
there are limitations to Tesseract’s detection of certain fonts, overall, it can successfully identify
words of basic fonts. Contour detection is an advanced technique that can be utilized to
measure an image. Shapes and mathematical calculations are crucial in determining the precise
location of the points on which to draw the body and forewing lines of the butterfly. Overall,
92% accuracy were achieved by the program for the set of butterflies measured.
SOCIAL MEDIA ANALYTICS FOR SENTIMENT ANALYSIS AND EVENT DETECTION IN SMART CI...cscpconf
Smart cities utilize Internet of Things (IoT) devices and sensors to enhance the quality of the city
services including energy, transportation, health, and much more. They generate massive
volumes of structured and unstructured data on a daily basis. Also, social networks, such as
Twitter, Facebook, and Google+, are becoming a new source of real-time information in smart
cities. Social network users are acting as social sensors. These datasets so large and complex
are difficult to manage with conventional data management tools and methods. To become
valuable, this massive amount of data, known as 'big data,' needs to be processed and
comprehended to hold the promise of supporting a broad range of urban and smart cities
functions, including among others transportation, water, and energy consumption, pollution
surveillance, and smart city governance. In this work, we investigate how social media analytics
help to analyze smart city data collected from various social media sources, such as Twitter and
Facebook, to detect various events taking place in a smart city and identify the importance of
events and concerns of citizens regarding some events. A case scenario analyses the opinions of
users concerning the traffic in three largest cities in the UAE
SOCIAL NETWORK HATE SPEECH DETECTION FOR AMHARIC LANGUAGEcscpconf
The anonymity of social networks makes it attractive for hate speech to mask their criminal
activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing
volume of social media data, hate speech identification becomes a challenge in
aggravating conflict between citizens of nations. The high rate of production, has become
difficult to collect, store and analyze such big data using traditional detection methods. This
paper proposed the application of apache spark in hate speech detection to reduce the
challenges. Authors developed an apache spark based model to classify Amharic Facebook
posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes
for learning and Word2Vec and TF-IDF for feature selection. Tested by 10-fold crossvalidation,
the model based on word2vec embedding performed best with 79.83%accuracy. The
proposed method achieve a promising result with unique feature of spark for big data.
GENERAL REGRESSION NEURAL NETWORK BASED POS TAGGING FOR NEPALI TEXTcscpconf
This article presents Part of Speech tagging for Nepali text using General Regression Neural
Network (GRNN). The corpus is divided into two parts viz. training and testing. The network is
trained and validated on both training and testing data. It is observed that 96.13% words are
correctly being tagged on training set whereas 74.38% words are tagged correctly on testing
data set using GRNN. The result is compared with the traditional Viterbi algorithm based on
Hidden Markov Model. Viterbi algorithm yields 97.2% and 40% classification accuracies on
training and testing data sets respectively. GRNN based POS Tagger is more consistent than the
traditional Viterbi decoding technique.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
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2. 186 Computer Science & Information Technology (CS & IT)
few reasons which makes this a challenging task are the problems arising from natural/dynamic
scene analysis; varying light conditions; different weather conditions; camera capturing
limitations; Effects of distortion, blur and occlusion; language/scripts used; and those challenges
raising with vehicle motions. In addition to these, some problems which cannot be addressed by
computer vision solutions are worn out number plates which are not illuminated well during
hours of darkness, dirty and broken etc. Most approaches therefore work only under restricted
conditions such as fixed illumination, limited vehicle speed, designated routes, and stationary
backgrounds.
This paper presents an algorithm to recognize number plates from natural scenes. The proposed
method comprises of three main stages, localization of the number plate, character segmentation,
and character recognition. Localisation stage includes binarization and noise elimination; locating
the number plate region; extracting the number plates. An adaptive threshold based global
binarization and locally applied Otsu’s binarization are combined to obtain a more accurate
binarization which retains the number plate and its characters intact. Localization & extraction of
the number plate follows projection profile based approach. This approach helps to identify the
possible regions of number plates in the images. These regions are further examined to select
number plate region for further processing or to discard. This technique helps to localize and
extract multiple number plates present in a single image.
In character segmentation, projection profile technique, in addition to an approximation algorithm
is used to segment each character from the number plate. For character recognition, Support
Vector Machine (SVM) is used. The segmented characters are classified into 36 classes, 26
classes of alphabets and 10 classes of numerals. The performance of the system has been
computed at each stage and is found to be around 97.1%, 95.4% and 95.72%. The overall
accuracy of the system is 92.68%.
The remaining of the paper is organized as follows. A brief background of related work is given
in Section II. The proposed algorithm is described in Section III. Details of database are given in
Section IV, Experimental results and comparison study is presented in Section V. Discussion and
conclusions are drawn in Section VI.
2. RELATED WORK
Salter in 1984 [1], presented the potential applications of automatic vehicle identification for
vehicle weighing and classification. Dickinson and Waterfall [2], in the same conference
presented a general discussion on the suitability of image/video processing to perform collection
of data, automatic surveillance, automatic incident detection, vehicle tracking, and vehicle
classification. Since, then many researchers [1] - [26] have worked towards addressing various
challenges in Number plate recognition.
Anagnostopoulos et al. [3], gives a survey on the work carried out till 2007. The article
categorizes and assesses numerous techniques developed for license plate recognition in still
images or video sequences. Shan Du et al., [7], present a comprehensive review of the state-of-
the-art techniques for Automatic License Plate Recognition (ALPR) till 2013. The methods are
categorized into different ALPR techniques according to the features the methods used at each
stage, and compare them in terms of pros, cons, recognition accuracy, and processing speed.
3. Computer Science & Information Technology (CS & IT) 187
Suresh et al., [4], proposed a novel method to enhance license plate numbers of moving vehicles
in real traffic videos. Clemens et al., [5] present full-featured license plate detection and
recognition system implemented on an embedded DSP platform processing video streams in real-
time. Shapiro et al., [6], proposed an inexpensive automatic solution for remote vehicle
identification. The recognition scheme combined adaptive iterative threshold with a template-
matching algorithm. In [8], Chang et al., has attempted to take care of some restricted working
environment conditions. The technique use fuzzy disciplines to extract license plates from an
input image, and neural network aims to identify the number present in a license plate.
Azad and Shayegh [9], use adaptive threshold to obtain binary images and then use edge
detection and morphological operations to localize number plates. Hsu et al., [10] proposed edge
clustering mechanism for number plate detection, maximally stable extreme region (MSER)
detector for character segmentation and LDA based character recognition. Chaudhary and
Chincore [11] proposed 8-neighbor connectivity analysis to localize multiple number plates in
Indian road conditions. Li et al., [12] used MSER to detect candidate characters and designed
conditional random field models and through belief propagation inference estimated the license
plate location. Carballido et al., [13] proposed a template matching approach to recognize license
plate digits in outdoor parking entrance vehicles. Wen et al [15] proposed a method for number
plate recognition which uses Bernsen’s algorithm to binaries the images and connected
component analysis to localize number plate regions and finally SVM to recognize the characters.
Comeli et al., [16] have proposed a complete solution to recognize number plates by localizing
the number plate through maximum local contrast, enhancing the images through Gaussian filters
and histogram stretching, detecting and correcting tilt and finally recognizing characters through
template matching. Many researchers have worked on number plate recognition of multi-style
and multi-nations. Shiying et al [14], proposed a decision tree based localization of multinational
license plate. Jiao et al., [20] proposed a morphology driven method for multi-style, multinational
license plate recognition. Thome et al., [21], used gradient density to localize number plates and
used hierarchical neural network to perform character recognition through classification. Guo and
Liu [22], proposed a self-learning and hybridized technique for license plate localization. Al-
Ghaili et al., [23] proposed license plate detection through the study of vertical line structures in
the image. Naito et al., [19] proposed a robust license plate recognition system capable of
recognizing number plates of moving vehicles in outdoors using wide dynamic range cameras.
Chang [17] also proposed a line detection and projection based method to localize number plates
and normal factors to recognize characters. Wang and Liu [18] used morphological operators and
connected component analysis to localize the number plates. Poon et al., [25] also used several
greyscale morphological operators to localize number plates. Sirithinaphong and Chamnongthai
[24], used projection profiles to localize number plates and then used back propagation neural
network to classify the characters.
The existing license plate recognition systems address the problem of recognition through three
sub-tasks viz., license plate localization, character segmentation and character recognition.
Similarly, in the next section we present in detail the algorithm we propose to recognize Indian
number plates in outdoor scenes.
3. PROPOSED SYSTEM
The proposed system comprises three main stages, localization, character segmentation &
normalization, and recognition.
4. 188 Computer Science & Information Technology (CS & IT)
3.1. Number Plate Localisation
The localisation stage focuses on detecting the number plate region from the captured image, and
comprises of sub-stages, binarization, Noise removal through filtering, and region cropping.
3.2. Binarization
Let ܫ be an RGB image (Fig-1).ܫ is converted into 8-bit gray scale image (Fig-2) ܩ by using the
standard weights set by NTSC (National Television System Committee) which is given below.
ܩሺ,ݑ ݒሻ = 0.229ܴ + 0.587ܩ + 0.114ܤ ሺ1ሻ
Figure 1. Input RGB image I
Figure 2. Gray scale image G of Fig.1
Figure 1. Binarized image B, using Otsu Global thresholding
5. Computer Science & Information Technology (CS & IT) 189
The gray scale image ܩ is then converted into binary image,ܤ using well known Otsu’s global
threshold method (Fig-3). It was observed that while performing Otsu’s method, although the
image is pretty noiseless, the number plate gets eliminated due to global threshold. If the
threshold is performed on local regions, the process can help to retain the number plate. To
perform local threshold, G is divided into blocks of size 50 × 50 and Otsu’s binarization is
performed on each window of 50 × 50. Likewise window is shifted to acquire next 50 × 50
region of ܩ to perform the binarization iteratively on all regions. The output of applying
Ostu’sbinarization on the image by considering local regions of 50x50 is shown in Fig-4. This
local thresholding will introduce some noise along with retaining the number plate.
Global thresholding provided comparatively noiseless image with no or some part of number
plate whereas local thresholding helped us to get the number plate but with more noise.
Figure 4. Binarized image C, using Otsu's method on local regions
3.3. Connected Component Based Filtering
The resultant image produced in binarization stage is noisy in nature. To eliminate the unwanted
noise we perform a connected component based filtering. The connected components are
examined if they are part of the number plate or not. Based on it verification the component is
retained or removed.
It has been observed from the number plates that after binarization, the characters of the number
plate are surrounded by good amount of white pixel region. This is one of the characteristics of
number plates, having dark characters over lighter backgrounds. The rectangular structure of the
plate contains darker characters over the lighter background which is a mandate. The
characteristic feature of having white pixels around the characters of the number plate that can be
seen in C, is used to localize the number plate. Fig-5 shows an example of 8-neighbor connected
component.
Figure 5. 8-Neighbor connectivity
To qualify a connected component as a possible number plate character we perform the
following.
6. 190 Computer Science & Information Technology (CS & IT)
Figure 2. 8 neighbour connected component analysis starting from the border pixel
The image C is scanned at the borders for a black pixel. Once a black pixel if found, this pixel is
used as a seed to find all its 8 connected neighbours, i.e., while scanning the image if any border
pixel Bp, is found to be black then we place a3 × 3 window in such a way so that the pixel Bp
becomes the center of the 3 × 3 window. Let this Bp be referred as the origin border pixel. Then
the 8-neighbors which are also black and are connected to Bp are noted. Any one of the
neighbours which is connected to Bp and is black in color becomes next center. The procedure is
recursively repeated over all black pixels which are connected to Bp. All these pixels which form
a region connected to the origin border pixel are eliminated, by changing them from black to
white. For illustration we consider the below matrix, shown in Fig.6, where 0 represents the black
pixel as the foreground and 1 represents the white pixel as the background.
In the above figure Fig-8 it is clearly observed that pixels (0,0), (1,1), (2,1), (2,3), (2,4), (3,3) and
(3,4) are black pixels and among these pixels only (0,0) is the border pixel. So pixel (0,0) is made
seed pixel. Now the 8 neighbour pixels of (0,0) which are also black are found. The pixel at (1,1)
is found to be black, and again 8 connected neighbour analysis is done for (1,1). The process
recursively continues till no more new connections are found. For the border pixel (0,0), (1,1) and
(2,1) are found as the 8-connected neighbours. This region of three pixels is eliminated by
replacing them by white pixels. This sequence is pictorially illustrated in Fig. 7.
(a)
(b)
7. Computer Science & Information Technology (CS & IT) 191
(c)
Figure 7. 8-neighbor connected component analysis
The effect of this connected component based filtering, over a small region is shown in Fig-8.
Figure 8. Effect of 8-neighbor connected component analysis
This 8 neighbour connected component analysis based filtering helps in getting a significantly
noiseless image (ܦFig-9).
Figure 9. Connected Component based filtered image D of input image C
3.4. Horizontal Black Run-length based Image Filtering
Though connected component analysis based filter removes significant amount of noise, some
noises which are disconnected from the image border are generally not removed. Connected
component analysis based filters starts from the border pixels and eliminates all pixels connected
to the border pixels. This leaves some connected components which are present towards the
center of the image which are disconnected from border pixels to be retained although they do not
pertain to be number plate components. To remove such noises yet another filtering approach is
proposed which performs at each row level.
8. 192 Computer Science & Information Technology (CS & IT)
Figure 10. Horizontal Run-length based Image Filtering E of input image D
Each row is scanned for continuous connected black pixels. A single row may have more than
one black connected component. If the pixel count of these black run-length components counts
is greater than 3% of the image width ሺVሻ then corresponding black pixels are converted to white.
Also again we check the total black pixel count for each row, if the count gets less than the 5% or
greater than the 80% of the image width of E then the entire row is made white.
A survey was carried out within our dataset and it is found that the width of any character present
in the number plate is less than the 3% of the width of the image for resolution 1456x2592. This
hypothesis helped us to get images with significantly less noise. On the other hand, if we observe
that a row contains black pixels less than 5% or greater than 80% of the width of an image then
we conclude that either the row doesn’t have any part of the number plate or the row is full of
noise. The effect of this image filtering technique has been furnished in Fig-10.
3.5. Number plate region extraction
While analyzing the pre-processed image E, it was clearly observed that the image can be
segmented into a number of sub-images. The sub-images can be further analyzed to check the
presence of the number plate. Along with number plate, sub-images may contain some additional
noise components. Horizontal projection profile is used to segment the entire image into sub-
images for further analysis. The horizontal projection profile for image E is shown in Fig-10.
Figure 11. Horizontal Black Profile Projection of E
9. Computer Science & Information Technology (CS & IT) 193
From the plot shown in Fig.11, if we find out the drastic shifts from zero and a drastic drop to
zero, with the rise and a drop forming one region, in the above profile, we can see four sub-
images. The number plate can be present in any of these four regions. Sometime, if the number
plate runs over two lines, then part of the number plate can be present in two separate sub-
images, which are adjacent to each other. The other sub-images can be discarded as irrelevant.
We have to crop these regions from the image E by inspecting the projection profile. From our
experimental observation, sub-images that correspond to noise or irrelevant regions tend to have
lesser black pixels density than sub-images that correspond(s) to number plate region.
First we calculate the average black pixel density of the image E using equation (1).
ܯ =
ே௨ ௫௦ ா
ௌ௭ ா
...(Eq.1)
From the horizontal profile the maxima point of each dense segment is computed. If the maxima
of any segment is higher or equal to M, i.e, the mean black pixel density of entire image, then the
sub-image corresponding to that segment of the projection profile is considered to have number
plate. However, we also need to ensure that the width of these sub-images is also acceptable. For
this, we must calculate width of each segment in the projection profile. If the width of the
segment is less than 2% of the length (Number of rows) of original image E then the sub-image
corresponding to that segment of project profile can be discarded.
Figure 12. Extracted all probable regions of number plate
4. CHARACTER SEGMENTATION
From the above sub-images, to segment out the characters we perform two validating criteria
explained below.
4.1. Projection Profile Based Validation
If the extracted sub-images are observed separately then it can be seen that the characters are
separated by a very few white pixels and generally they maintain an equidistance from each other
whereas the regions with full of noise do not have such observations. To achieve this, vertical
black projection profile has been applied on extracted sub-images to determine the high density
black region. One such projection profile is shown in Figure 13.
From the Fig-13, it is very clear that centre peak region represents high density black regions
which can be regarded as characters of number plate, if we get black pixel peaks in the alternate
segments separated by equidistant segments corresponding to background, until a long stretch of
drop to zero is encountered.
10. 194 Computer Science & Information Technology (CS & IT)
Figure 13. Vertical Black Projection Profile of a sub-image consisting of Number Plate
4.2. Character Height and Width Validation
As per our survey on our dataset, it has been observed that if the distance between camera and the
car is nearly 10 meters and the resolution of the captured image is 1456x2592 then on an average
a valid character on the number plate must contain at least 100 black pixels. Each sub-image is
examined separately to discard connected components with size less than 100 pixels. Some of the
outcomes of eliminating connected components of size less than 100 pixels for some sub-images
are shown in Fig.14.
In addition to the above observation, the size of the characters of the number plate is also uniform
in nature. The individual height and width of those character segments will be approximately
same. After, eliminating some unwanted smaller components, a minimum bounding rectangle is
fit over all remaining connected components.
Figure 14. Removal of connected components of smaller size
The minimum bounding rectangles fit around the connected components corresponding to the
characters belonging to number plate region, will approximately have the same height and width.
It can also be observed that the, begin and end of these rectangles will be aligned to the same
point on the Y-axis. So if there are at least 4 or more rectangles which are adjacent to each other
and have the properties explained above, then these rectangles can be selected as relevant. We
choose 4 rectangles due to the reason; the number plate characters may be spread in 2 lines, with
a maximum of four characters in each line. Another criteria, that is employed to check if the
segmented characters belong to the number plate is by comparing if the region of these characters
match with the region which correspond to the dense projection profile generated in the previous
stage.
11. Computer Science & Information Technology (CS & IT) 195
Using the above two validation criteria, the character and the number plate regions are segmented
and separated from the other irrelevant components as shown in Fig. 15. This segmented region
along with the segmented characters is then passed to the character recognition stage.
Figure 15. Segments after Employing Height-Width Approximation method
(a)
(b)
Figure 16. Segments after Employing White Pixel Ratio over Black Pixel
In order to make sure that the classification stage receives only characters (Alphabets and
numerals as inputs), white to black pixel ratio for each connected component enclosed in the
minimum bounding rectangle is calculated. If the character enclosed in the rectangle has a ratio
greater than 4 (refers to segment maximum white) or equal to 0 (refers to segment full black) then
the segment is discarded. This technique produces noise free character segments only. An
example of such case and the result after the elimination of the noisy segments is shown in Fig.16
(a) and (b). The non-alphabetical characters enclosed in minimum bounding rectangles are just
simulated noise. If the total number of characters segmented is less than 4, it is discarded from
next stage of recognition and classification. The segmented characters are normalized to a new
scale of dimension (40 × 20) before sent for training and classification.
5. OPTICAL CHARACTER RECOGNITION
In this stage, Support Vector Machine (SVM) was used for supervised learning of 36 classes (26
alphabets of upper case and 10 digits).During dataset generation, it was observed that number
plate generally contains alphabets in upper case only so the same data for lower case alphabets
are not considered.
For character recognition, the entire character enclosed by a minimum bounding rectangle is used
for training. The image is vectorised by re-arranging the pixel values into one dimensional vector
K of size,1 × 800. Let F represents total number of vectors. These FxK feature vectors are
submitted to SVM for training. The segmented characters from the number plate are then
classified using the trained classifier, corresponding ASCII values are written in a text file to
achieve complete OCR.
6. DATABASE
We have tested our proposed algorithm on 560 different images of license plates. As earlier
mentioned, all images are captured at a distance nearly 10meters from the vehicle. The resolution
12. 196 Computer Science & Information Technology (CS & IT)
of all captured images is 1456x2592 and they are saved in standard JPEG format. Apart from this
database, to establish the proposed method as resolution invariant we have scaled down and
scaled up the images in different resolutions and performed the same operation. Detailed
discussion is given in the next section.
7. EXPERIMENTAL RESULTS AND COMPARISON STUDY
To test the resolution invariant feature 100 images have been scaled down and scaled up in
different resolution and employed with the proposed method. It has been observed that the
performance of proposed method was accurate in extracting number plates and character
segmentation for various sizes of images shown in Table-1.
Table1: Different image sizes on which the number plate extraction and character segmentation was tested.
The overall performance of the proposed method compared to other methods with respect to
number plate extraction, character recognition is shown in Table-2, and Table-3. The accuracy of
character segmentation is 95.4%.
It is to be mentioned that the proposed approach is capable of recognizing double lined number
plate. An example of such example has been shown in Fig-21.
As the number plate extraction algorithm extracts all the probable number plate regions, so it is
possible to recognize multiple number plates from a single image or a double line number plate.
An Example of such case is given below in Fig-22 using a synthetic dataset.
Table 2: Performance comparison of the proposed system for number plate extraction
Methods Number Plate
Extraction (%)
Lee et al. [26] 94.4
Chiou et al. [27] 96.2
Shi et al. [28] 96.5
Wang et al. [29] 98
Chang et al. [30] 98
Deb et al. [31] 92.4
Jia et al. [32] 95.6
Kim et al. [33] 93.5
Duan et al. [34] 93.6
Roy et al. [35] 91.59
Proposed 97.1
13. Computer Science & Information Technology (CS & IT) 197
Table 3: Performance comparison of the proposed system for number plate recognition
Methods OCR Rate (%)
Lee et al. [26] 95.7
Shi et al. [28] 89.1
Chang et al. [30] 94.2
Proposed 95.72
8. DISCUSSION AND CONCLUSION
The above proposed system performs efficiently for wide variations in illumination conditions
and different types of number plates. It has the features like double line and multiple number
plate recognition. Though there are certain restrictions in this system like- different font style
(e.g. italics) and colors of the number plate, excessive skewed number plate, which we consider
for our future work.
Figure 17. Successful Extraction of Double Lined Number Plate
Figure 18. Detection of Multiple Number Plates from Single Input Image
14. 198 Computer Science & Information Technology (CS & IT)
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