License plate recognition (LPR) is one of the classical problems in the
field of object recognition. Its application is very crucial in the
automation of transportation system since it helps to recognise a vehicle
identity, which information is stored in the license plate. LPR usually
consists of three major phases: pre-processing, license plate localisation,
optical character recognition (OCR). Despite being classical, its
implementation faced with much more complicated problems in the real
scenario. This paper proposed an improved LPR algorithm based on
modified horizontal-vertical edge projection. The method uses for
detecting and localising the region of interest. It is done using the
horizontal and vertical projection of the image. Related works proved
that the modified horizontal-vertical edge projection is the simplest
method to be implemented, yet very effective against Indonesian license
plate. However, its performance gets reduced when specular reflection
occurs in the sample image. Therefore, morphological operations are
utilised in the pre-processing phase to reduce such effects while
preserving the needed information. Eighty sample images which
captured using various camera configurations were used in this research.
Based on the experimental results, our proposed algorithm shows an
improvement compared with the previous study and successfully detect
71 license plates in 80 image samples which results in 88.75% accuracy.
IRJET - Efficient Approach for Number Plaque Accreditation System using W...IRJET Journal
This document presents a proposed efficient approach for number plaque recognition system using Android devices. It discusses using optical character recognition and image processing techniques to extract vehicle number plates from images and recognize the characters. The system is designed to identify vehicles for applications like toll plazas, parking areas, and secure areas by automatically recognizing license plates from moving vehicles. It compares different methods like template matching and neural networks for the character recognition component. The proposed system aims to provide a user-friendly Android application to enable contactless verification of vehicle documents using Aadhaar card numbers, reducing the need to manually carry documents. It is intended to improve security, identify vehicles violating traffic rules, and reduce the complexity and time of existing systems.
This document summarizes an article from the International Journal of Research in Advent Technology that proposes an enhanced automatic license plate recognition system. The system aims to overcome issues with existing ALPR techniques like uneven illumination and poor image quality. It develops an ALPR system with four main stages: image acquisition, license plate extraction, license plate segmentation, and character recognition. A key contribution is a methodology to enhance images by eliminating illumination issues before processing, which improves accuracy. The document reviews related literature on existing ALPR techniques and compares their advantages and disadvantages. It then describes the proposed system and experimental results demonstrating its effectiveness at license plate recognition.
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.
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%.
IRJET- Recognition of Indian License Plate Number from Live Stream VideosIRJET Journal
This document presents a survey of existing automatic number plate recognition (ANPR) systems and proposes a new approach using k-nearest neighbors (k-NN), OpenALPR, and convolutional neural networks (CNNs). It first discusses challenges with license plate recognition in India due to regional fonts. Existing ANPR systems are reviewed along with their drawbacks. The proposed method involves detecting license plates in images and videos, extracting characters, and recognizing plates using k-NN, OpenALPR and a CNN model. Results of the three approaches on test data will be analyzed to determine the most accurate one, considering factors like character recognition accuracy and processing time.
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).
The document summarizes research on license plate recognition (LPR) systems. It discusses how LPR works by using computer vision algorithms to detect vehicle license plates in images and then perform optical character recognition (OCR) to read the characters. The key steps involve preprocessing images using techniques like grayscale conversion and edge detection. Contour detection is then used to localize the license plate region. Character segmentation and recognition follows using methods like neural networks. The document reviews various existing LPR techniques and algorithms reported in other research papers. It then outlines the objectives and methodology of LPR, including preprocessing, binarization, edge detection with filters, contour detection to extract plates, and OCR to read characters. Python is used to demonstrate an
Automatic And Fast Vehicle Number Plate Detection with Owner Identification U...IRJET Journal
This document discusses a proposed system for automatic vehicle number plate detection and owner identification using neural networks. The system aims to detect license plates from images, extract the plate region, segment characters, recognize the characters using OCR, and check owner details in a database by matching the recognized plate number. The system is designed to address issues with existing manual tracking systems and proprietary ALPR systems, using open-source technologies and real-time processing. Key stages of the proposed system include license plate detection using YOLOv3, plate segmentation, character recognition with OCR, and displaying owner details by matching the plate number to a database.
IRJET - Efficient Approach for Number Plaque Accreditation System using W...IRJET Journal
This document presents a proposed efficient approach for number plaque recognition system using Android devices. It discusses using optical character recognition and image processing techniques to extract vehicle number plates from images and recognize the characters. The system is designed to identify vehicles for applications like toll plazas, parking areas, and secure areas by automatically recognizing license plates from moving vehicles. It compares different methods like template matching and neural networks for the character recognition component. The proposed system aims to provide a user-friendly Android application to enable contactless verification of vehicle documents using Aadhaar card numbers, reducing the need to manually carry documents. It is intended to improve security, identify vehicles violating traffic rules, and reduce the complexity and time of existing systems.
This document summarizes an article from the International Journal of Research in Advent Technology that proposes an enhanced automatic license plate recognition system. The system aims to overcome issues with existing ALPR techniques like uneven illumination and poor image quality. It develops an ALPR system with four main stages: image acquisition, license plate extraction, license plate segmentation, and character recognition. A key contribution is a methodology to enhance images by eliminating illumination issues before processing, which improves accuracy. The document reviews related literature on existing ALPR techniques and compares their advantages and disadvantages. It then describes the proposed system and experimental results demonstrating its effectiveness at license plate recognition.
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.
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%.
IRJET- Recognition of Indian License Plate Number from Live Stream VideosIRJET Journal
This document presents a survey of existing automatic number plate recognition (ANPR) systems and proposes a new approach using k-nearest neighbors (k-NN), OpenALPR, and convolutional neural networks (CNNs). It first discusses challenges with license plate recognition in India due to regional fonts. Existing ANPR systems are reviewed along with their drawbacks. The proposed method involves detecting license plates in images and videos, extracting characters, and recognizing plates using k-NN, OpenALPR and a CNN model. Results of the three approaches on test data will be analyzed to determine the most accurate one, considering factors like character recognition accuracy and processing time.
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).
The document summarizes research on license plate recognition (LPR) systems. It discusses how LPR works by using computer vision algorithms to detect vehicle license plates in images and then perform optical character recognition (OCR) to read the characters. The key steps involve preprocessing images using techniques like grayscale conversion and edge detection. Contour detection is then used to localize the license plate region. Character segmentation and recognition follows using methods like neural networks. The document reviews various existing LPR techniques and algorithms reported in other research papers. It then outlines the objectives and methodology of LPR, including preprocessing, binarization, edge detection with filters, contour detection to extract plates, and OCR to read characters. Python is used to demonstrate an
Automatic And Fast Vehicle Number Plate Detection with Owner Identification U...IRJET Journal
This document discusses a proposed system for automatic vehicle number plate detection and owner identification using neural networks. The system aims to detect license plates from images, extract the plate region, segment characters, recognize the characters using OCR, and check owner details in a database by matching the recognized plate number. The system is designed to address issues with existing manual tracking systems and proprietary ALPR systems, using open-source technologies and real-time processing. Key stages of the proposed system include license plate detection using YOLOv3, plate segmentation, character recognition with OCR, and displaying owner details by matching the plate number to a database.
License Plate Recognition System for Moving Vehicles Using Laplacian Edge De...IRJET Journal
This document presents a license plate recognition system for moving vehicles that uses the Laplacian edge detector and feature extraction. The system first applies Otsu's method to binarize the captured vehicle image and reduce noise using a median filter. It then uses the Laplacian operator to detect edges and locate the license plate region. The characters in the plate are segmented and normalized. Finally, features are extracted from the characters for recognition, allowing the system to handle characters written in English or Hindi scripts. The proposed approach aims to address challenges in recognizing Indian license plates, which can vary in format and contain text in multiple languages.
AUTOMATIC LICENSE PLATE RECOGNITION USING YOLOV4 AND TESSERACT OCRAngie Miller
The document presents a method for automatic license plate recognition (ALPR) using YOLOv4 and Tesseract OCR. It has three main steps:
1) Training a dataset of license plate images using YOLOv4, a real-time object detection system, to detect license plates and generate bounding boxes around them with 92% accuracy.
2) Applying image processing techniques like grayscale conversion, Gaussian blurring, thresholding, and morphological operations to segment characters from the detected license plates.
3) Recognizing the characters using Tesseract OCR after pre-processing, achieving 81% accuracy on character recognition.
The proposed approach obtains high accuracy on license plate detection and character
Number Plate Recognition of Still Images in Vehicular Parking SystemIRJET Journal
This document discusses a proposed method for number plate recognition in vehicle parking systems using image processing techniques. It begins with an abstract that outlines the increasing need for automated vehicle management systems due to rising vehicle and traffic volumes. It then provides an overview of the key steps in number plate recognition systems - plate detection, character segmentation, and character recognition. The proposed method uses profile projection for segmentation and neural networks for recognition. The document reviews several existing plate detection methods and their limitations. It proposes a new method that uses edge detection and morphological operations to isolate the license plate from an image while removing noise. Finally, it discusses factors to consider for license plate detection and different image segmentation techniques used in existing automatic number plate recognition systems.
This document describes a license plate recognition system developed by students at CEC, Karnataka, India. It begins with an abstract that outlines license plate recognition and the key steps: capturing images, pre-processing, segmentation, and character recognition using Python. The introduction provides more details on license plate recognition systems and their uses. A literature review summarizes 10 relevant papers on license plate recognition techniques. The proposed system is then described, outlining the steps of image acquisition, desaturation, thresholding, morphological operations, segmentation, and character recognition.
Automated Identification of Road Identifications using CNN and KerasIRJET Journal
The document proposes a model to automatically detect traffic signs using convolutional neural networks (CNN) and the Keras library, even if the signs are unclear or damaged. It aims to help autonomous vehicles properly identify different types of traffic signs. The methodology involves collecting a dataset of traffic sign images, training a CNN model using Keras, testing the model on new images, and using the trained model to recognize signs from user-provided inputs in real-time. Evaluation metrics like accuracy and loss are plotted to analyze the model's performance. The system is meant to achieve over 95% accuracy in identifying various traffic sign types to assist self-driving cars in safely following traffic rules.
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.
Automated Generation for Number Plate Detection and RecognitionIRJET Journal
This document discusses an automated system for vehicle number plate detection and recognition. It proposes an efficient method for recognizing Indian vehicle number plates that addresses issues of scaling and character position recognition with high accuracy. The system aims to design an automated and accurate vehicle identification system using Indian vehicle number plates for applications like automatic toll collection, parking management, border checkpoints, and traffic control. It discusses various stages of the system including number plate localization, character segmentation, and number plate recognition. The proposed system is suitable for non-standard Indian plates with single or double line plates in varying lighting conditions and can handle multilingual, multicolor plates according to Indian conditions.
IRJET- Advenced Traffic Management System using Automatic Number Plate Recogn...IRJET Journal
This document describes an advanced traffic management system using automatic number plate recognition (ANPR). It discusses how current vehicle tracking systems have limitations in identifying fake number plates. The proposed system uses image processing and computer vision techniques to identify the number plate and type of vehicle from images or video. It extracts the number plate using edge detection and morphological operations. Optical character recognition and template matching are then used to recognize the characters. A convolutional neural network classifies the vehicle type. The system can check if a number plate is fake by comparing with an RTO database, and alert police if a match is found to a wanted vehicle number. It aims to help police track vehicles of interest and improve traffic management.
IRJET- Smart Parking System using Facial Recognition, Optical Character Recog...IRJET Journal
The document proposes a smart parking system using facial recognition, optical character recognition (OCR), and the Internet of Things (IoT). The system uses facial recognition to identify drivers and OCR to extract text from vehicle license plates to verify if the driver and vehicle match database records. An IoT device is integrated with parking gates to automatically open them if verification is successful. The system aims to optimize parking space usage, enhance security, reduce congestion and emissions, and make the parking process more convenient. It analyzes previous research on smart parking and aims to implement an accurate and cost-effective solution.
A design of license plate recognition system using convolutional neural networkIJECEIAES
This document describes a license plate recognition system using convolutional neural networks. The system uses several preprocessing techniques like Sobel edge detection, morphological operations, and connected component analysis to localize, isolate, and segment characters from license plate images. A convolutional neural network with a four-layer architecture is then used for character recognition. Various hyperparameters of the CNN model like the number of feature maps, connection patterns, and learning parameters are optimized using 10-fold cross-validation. The proposed system achieves 74.7% accuracy in the preprocessing stage and 94.6% accuracy in the character recognition stage using CNN.
A hierarchical RCNN for vehicle and vehicle license plate detection and recog...IJECEIAES
Vehicle and vehicle license detection obtained incredible achievements during recent years that are also popularly used in real traffic scenarios, such as intelligent traffic monitoring systems, auto parking systems, and vehicle services. Computer vision attracted much attention in vehicle and vehicle license detection, benefit from image processing and machine learning technologies. However, the existing methods still have some issues with vehicle and vehicle license plate recognition, especially in a complex environment. In this paper, we propose a multivehicle detection and license plate recognition system based on a hierarchical region convolutional neural network (RCNN). Firstly, a higher level of RCNN is employed to extract vehicles from the original images or video frames. Secondly, the regions of the detected vehicles are input to a lower level (smaller) RCNN to detect the license plate. Thirdly, the detected license plate is split into single numbers. Finally, the individual numbers are recognized by an even smaller RCNN. The experiments on the real traffic database validated the proposed method. Compared with the commonly used all-in-one deep learning structure, the proposed hierarchical method deals with the license plate recognition task in multiple levels for sub-tasks, which enables the modification of network size and structure according to the complexity of sub-tasks. Therefore, the computation load is reduced.
Performance Evaluation of Automatic Number Plate Recognition on Android Smart...IJECEIAES
Automatic Number Plate Recognition (ANPR) is an intelligent system which has the capability to recognize the character on vehicle number plate. Previous researches implemented ANPR system on personal computer (PC) with high resolution camera and high computational capability. On the other hand, not many researches have been conducted on the design and implementation of ANPR in smartphone platforms which has limited camera resolution and processing speed. In this paper, various steps to optimize ANPR, including pre-processing, segmentation, and optical character recognition (OCR) using artificial neural network (ANN) and template matching, were described. The proposed ANPR algorithm was based on Tesseract and Leptonica libraries. For comparison purpose, the template matching based OCR will be compared to ANN based OCR. Performance of the proposed algorithm was evaluated on the developed Malaysian number plates’ image database captured by smartphone’s camera. Results showed that the accuracy and processing time of the proposed algorithm using template matching was 97.5% and 1.13 seconds, respectively. On the other hand, the traditional algorithm using template matching only obtained 83.7% recognition rate with 0.98 second processing time. It shows that our proposed ANPR algorithm improved the recognition rate with negligible additional processing time.
IRJET- Traffic Sign Detection, Recognition and Notification System using ...IRJET Journal
This document presents a traffic sign detection, recognition, and notification system using Faster R-CNN. The system takes video input containing traffic signs and converts it to frames. Faster R-CNN with ROI pooling and a classifier is used to detect traffic signs. Color and shape information are then used to refine detections. A CNN classifier recognizes the signs. The system notifies drivers of detected signs via audio messages, helping drivers comply with signs even if ignored visually. The proposed detector detects all sign categories, and recognition accuracy on the German Traffic Sign Detection Benchmark dataset exceeds 90% for 42 sign classes.
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
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.
Automated PCB identification and defect-detection system (APIDS)IJECEIAES
Ever growing PCB industry requires automation during manufacturing process to produce defect free products. Machine Vision is widely used as popular means of inspection to find defects in PCBs. However, it is still largely dependent on user input to select algorithm set for the PCB under inspection prior to the beginning of the process. Continuous increase in computation power of computers and image quality of image acquisition devices demands new methods for further automation. This paper proposes a new method to achieve further automation by identifying the type of PCB under inspection prior to begin defect inspection process. Identification of PCB is achieved by using local feature detectors SURF and ORB and using the orientation data acquired to transform the PCB image to the reference image for inspection of defects. A close-loop system is produced as a prototype to reflect the practicality of the idea. A Graphical User Interface was developed using MATLAB to present the proposed system. Test data contained 29 PCBs. Each PCB was tested 5 times for camera acquired images and 3 times for database images. The identification accuracy is 98.66% for database images and 100% for images acquired from the camera. The time taken to detect the model of PCB is recorded and is significantly lower for ORB based identification than SURF based. The system is also a close loop system which detects defects in PCB units. The detection of defects has highest accuracy of 92.3% for best controlled environment scenario. With controlled environment, the system could detect defects in PCB pertaining to smallest of components such as SMDs.
A Review: Machine vision and its ApplicationsIOSR Journals
Abstract:The machine vision has been used in the industrial machine designing by using the intelligent character recognition. Due to its increased use, it makes the significant contribution to ensure the competitiveness in modern development. The state of art in machine vision inspection and a critical overview of applications in various industries are presented in this paper. In its restricted sense it is also known as the computer vision or the robot vision. This paper gives the overview of Machine Vision Technology in the first section, followed by various industrial application and thefuture trends in Machine Vision. Keywords:CCD- charged coupled devices, Fruit harvesting system, HIS- Hue Saturation Intensity, Image analysis, Image enhancement, Image feature extraction, Image feature classification processing, Intelligent Vehicle tracking , Isodiscriminationn Contour, Machine Vision
Licence Plate Recognition Using Supervised Learning and Deep LearningIRJET Journal
1. The document discusses using supervised learning and deep learning techniques for license plate recognition (LPR). It analyzes direct and indirect recognition algorithms and compares features of existing LPR systems.
2. A proposed LPR system is described that uses image preprocessing, license plate detection, character segmentation, and character recognition. Preprocessing improves image quality before detecting the license plate region.
3. The proposed system applies contour tracing and Canny edge detection algorithms to the license plate region to sharpen character edges for recognition.
Automatic Number Plate Recognition System (ANPR) A Survey.pdfTina Gabel
This document provides a survey of different approaches to Automatic Number Plate Recognition (ANPR) systems. It discusses the common steps in ANPR systems, which include vehicle image capture, number plate detection, character segmentation, and character recognition. It then reviews various techniques that have been used for number plate detection, such as image binarization, edge detection, Hough transform, blob detection, and connected component analysis. The document surveys several papers that have implemented different approaches for number plate detection, including sliding concentric windows, configurable methods, and feature-based localization. Finally, it discusses methods that have been used for character segmentation and recognition.
Development of depth map from stereo images using sum of absolute differences...nooriasukmaningtyas
This article proposes a framework for the depth map reconstruction using stereo images. Fundamentally, this map provides an important information which commonly used in essential applications such as autonomous vehicle navigation, drone’s navigation and 3D surface reconstruction. To develop an accurate depth map, the framework must be robust against the challenging regions of low texture, plain color and repetitive pattern on the input stereo image. The development of this map requires several stages which starts with matching cost calculation, cost aggregation, optimization and refinement stage. Hence, this work develops a framework with sum of absolute difference (SAD) and the combination of two edge preserving filters to increase the robustness against the challenging regions. The SAD convolves using block matching technique to increase the efficiency of matching process on the low texture and plain color regions. Moreover, two edge preserving filters will increase the accuracy on the repetitive pattern region. The results show that the proposed method is accurate and capable to work with the challenging regions. The results are provided by the Middlebury standard dataset. The framework is also efficiently and can be applied on the 3D surface reconstruction. Moreover, this work is greatly competitive with previously available methods.
Model predictive controller for a retrofitted heat exchanger temperature cont...nooriasukmaningtyas
This paper aims to demonstrate the practical aspects of process control theory for undergraduate students at the Department of Chemical Engineering at the University of Bahrain. Both, the ubiquitous proportional integral derivative (PID) as well as model predictive control (MPC) and their auxiliaries were designed and implemented in a real-time framework. The latter was realized through retrofitting an existing plate-and-frame heat exchanger unit that has been operated using an analog PID temperature controller. The upgraded control system consists of a personal computer (PC), low-cost signal conditioning circuit, national instruments USB 6008 data acquisition card, and LabVIEW software. LabVIEW control design and simulation modules were used to design and implement the PID and MPC controllers. The performance of the designed controllers was evaluated while controlling the outlet temperature of the retrofitted plate-and-frame heat exchanger. The distinguished feature of the MPC controller in handling input and output constraints was perceived in real-time. From a pedagogical point of view, realizing the theory of process control through practical implementation was substantial in enhancing the student’s learning and the instructor’s teaching experience.
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AUTOMATIC LICENSE PLATE RECOGNITION USING YOLOV4 AND TESSERACT OCRAngie Miller
The document presents a method for automatic license plate recognition (ALPR) using YOLOv4 and Tesseract OCR. It has three main steps:
1) Training a dataset of license plate images using YOLOv4, a real-time object detection system, to detect license plates and generate bounding boxes around them with 92% accuracy.
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This document discusses a proposed method for number plate recognition in vehicle parking systems using image processing techniques. It begins with an abstract that outlines the increasing need for automated vehicle management systems due to rising vehicle and traffic volumes. It then provides an overview of the key steps in number plate recognition systems - plate detection, character segmentation, and character recognition. The proposed method uses profile projection for segmentation and neural networks for recognition. The document reviews several existing plate detection methods and their limitations. It proposes a new method that uses edge detection and morphological operations to isolate the license plate from an image while removing noise. Finally, it discusses factors to consider for license plate detection and different image segmentation techniques used in existing automatic number plate recognition systems.
This document describes a license plate recognition system developed by students at CEC, Karnataka, India. It begins with an abstract that outlines license plate recognition and the key steps: capturing images, pre-processing, segmentation, and character recognition using Python. The introduction provides more details on license plate recognition systems and their uses. A literature review summarizes 10 relevant papers on license plate recognition techniques. The proposed system is then described, outlining the steps of image acquisition, desaturation, thresholding, morphological operations, segmentation, and character recognition.
Automated Identification of Road Identifications using CNN and KerasIRJET Journal
The document proposes a model to automatically detect traffic signs using convolutional neural networks (CNN) and the Keras library, even if the signs are unclear or damaged. It aims to help autonomous vehicles properly identify different types of traffic signs. The methodology involves collecting a dataset of traffic sign images, training a CNN model using Keras, testing the model on new images, and using the trained model to recognize signs from user-provided inputs in real-time. Evaluation metrics like accuracy and loss are plotted to analyze the model's performance. The system is meant to achieve over 95% accuracy in identifying various traffic sign types to assist self-driving cars in safely following traffic rules.
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.
Automated Generation for Number Plate Detection and RecognitionIRJET Journal
This document discusses an automated system for vehicle number plate detection and recognition. It proposes an efficient method for recognizing Indian vehicle number plates that addresses issues of scaling and character position recognition with high accuracy. The system aims to design an automated and accurate vehicle identification system using Indian vehicle number plates for applications like automatic toll collection, parking management, border checkpoints, and traffic control. It discusses various stages of the system including number plate localization, character segmentation, and number plate recognition. The proposed system is suitable for non-standard Indian plates with single or double line plates in varying lighting conditions and can handle multilingual, multicolor plates according to Indian conditions.
IRJET- Advenced Traffic Management System using Automatic Number Plate Recogn...IRJET Journal
This document describes an advanced traffic management system using automatic number plate recognition (ANPR). It discusses how current vehicle tracking systems have limitations in identifying fake number plates. The proposed system uses image processing and computer vision techniques to identify the number plate and type of vehicle from images or video. It extracts the number plate using edge detection and morphological operations. Optical character recognition and template matching are then used to recognize the characters. A convolutional neural network classifies the vehicle type. The system can check if a number plate is fake by comparing with an RTO database, and alert police if a match is found to a wanted vehicle number. It aims to help police track vehicles of interest and improve traffic management.
IRJET- Smart Parking System using Facial Recognition, Optical Character Recog...IRJET Journal
The document proposes a smart parking system using facial recognition, optical character recognition (OCR), and the Internet of Things (IoT). The system uses facial recognition to identify drivers and OCR to extract text from vehicle license plates to verify if the driver and vehicle match database records. An IoT device is integrated with parking gates to automatically open them if verification is successful. The system aims to optimize parking space usage, enhance security, reduce congestion and emissions, and make the parking process more convenient. It analyzes previous research on smart parking and aims to implement an accurate and cost-effective solution.
A design of license plate recognition system using convolutional neural networkIJECEIAES
This document describes a license plate recognition system using convolutional neural networks. The system uses several preprocessing techniques like Sobel edge detection, morphological operations, and connected component analysis to localize, isolate, and segment characters from license plate images. A convolutional neural network with a four-layer architecture is then used for character recognition. Various hyperparameters of the CNN model like the number of feature maps, connection patterns, and learning parameters are optimized using 10-fold cross-validation. The proposed system achieves 74.7% accuracy in the preprocessing stage and 94.6% accuracy in the character recognition stage using CNN.
A hierarchical RCNN for vehicle and vehicle license plate detection and recog...IJECEIAES
Vehicle and vehicle license detection obtained incredible achievements during recent years that are also popularly used in real traffic scenarios, such as intelligent traffic monitoring systems, auto parking systems, and vehicle services. Computer vision attracted much attention in vehicle and vehicle license detection, benefit from image processing and machine learning technologies. However, the existing methods still have some issues with vehicle and vehicle license plate recognition, especially in a complex environment. In this paper, we propose a multivehicle detection and license plate recognition system based on a hierarchical region convolutional neural network (RCNN). Firstly, a higher level of RCNN is employed to extract vehicles from the original images or video frames. Secondly, the regions of the detected vehicles are input to a lower level (smaller) RCNN to detect the license plate. Thirdly, the detected license plate is split into single numbers. Finally, the individual numbers are recognized by an even smaller RCNN. The experiments on the real traffic database validated the proposed method. Compared with the commonly used all-in-one deep learning structure, the proposed hierarchical method deals with the license plate recognition task in multiple levels for sub-tasks, which enables the modification of network size and structure according to the complexity of sub-tasks. Therefore, the computation load is reduced.
Performance Evaluation of Automatic Number Plate Recognition on Android Smart...IJECEIAES
Automatic Number Plate Recognition (ANPR) is an intelligent system which has the capability to recognize the character on vehicle number plate. Previous researches implemented ANPR system on personal computer (PC) with high resolution camera and high computational capability. On the other hand, not many researches have been conducted on the design and implementation of ANPR in smartphone platforms which has limited camera resolution and processing speed. In this paper, various steps to optimize ANPR, including pre-processing, segmentation, and optical character recognition (OCR) using artificial neural network (ANN) and template matching, were described. The proposed ANPR algorithm was based on Tesseract and Leptonica libraries. For comparison purpose, the template matching based OCR will be compared to ANN based OCR. Performance of the proposed algorithm was evaluated on the developed Malaysian number plates’ image database captured by smartphone’s camera. Results showed that the accuracy and processing time of the proposed algorithm using template matching was 97.5% and 1.13 seconds, respectively. On the other hand, the traditional algorithm using template matching only obtained 83.7% recognition rate with 0.98 second processing time. It shows that our proposed ANPR algorithm improved the recognition rate with negligible additional processing time.
IRJET- Traffic Sign Detection, Recognition and Notification System using ...IRJET Journal
This document presents a traffic sign detection, recognition, and notification system using Faster R-CNN. The system takes video input containing traffic signs and converts it to frames. Faster R-CNN with ROI pooling and a classifier is used to detect traffic signs. Color and shape information are then used to refine detections. A CNN classifier recognizes the signs. The system notifies drivers of detected signs via audio messages, helping drivers comply with signs even if ignored visually. The proposed detector detects all sign categories, and recognition accuracy on the German Traffic Sign Detection Benchmark dataset exceeds 90% for 42 sign classes.
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
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.
Automated PCB identification and defect-detection system (APIDS)IJECEIAES
Ever growing PCB industry requires automation during manufacturing process to produce defect free products. Machine Vision is widely used as popular means of inspection to find defects in PCBs. However, it is still largely dependent on user input to select algorithm set for the PCB under inspection prior to the beginning of the process. Continuous increase in computation power of computers and image quality of image acquisition devices demands new methods for further automation. This paper proposes a new method to achieve further automation by identifying the type of PCB under inspection prior to begin defect inspection process. Identification of PCB is achieved by using local feature detectors SURF and ORB and using the orientation data acquired to transform the PCB image to the reference image for inspection of defects. A close-loop system is produced as a prototype to reflect the practicality of the idea. A Graphical User Interface was developed using MATLAB to present the proposed system. Test data contained 29 PCBs. Each PCB was tested 5 times for camera acquired images and 3 times for database images. The identification accuracy is 98.66% for database images and 100% for images acquired from the camera. The time taken to detect the model of PCB is recorded and is significantly lower for ORB based identification than SURF based. The system is also a close loop system which detects defects in PCB units. The detection of defects has highest accuracy of 92.3% for best controlled environment scenario. With controlled environment, the system could detect defects in PCB pertaining to smallest of components such as SMDs.
A Review: Machine vision and its ApplicationsIOSR Journals
Abstract:The machine vision has been used in the industrial machine designing by using the intelligent character recognition. Due to its increased use, it makes the significant contribution to ensure the competitiveness in modern development. The state of art in machine vision inspection and a critical overview of applications in various industries are presented in this paper. In its restricted sense it is also known as the computer vision or the robot vision. This paper gives the overview of Machine Vision Technology in the first section, followed by various industrial application and thefuture trends in Machine Vision. Keywords:CCD- charged coupled devices, Fruit harvesting system, HIS- Hue Saturation Intensity, Image analysis, Image enhancement, Image feature extraction, Image feature classification processing, Intelligent Vehicle tracking , Isodiscriminationn Contour, Machine Vision
Licence Plate Recognition Using Supervised Learning and Deep LearningIRJET Journal
1. The document discusses using supervised learning and deep learning techniques for license plate recognition (LPR). It analyzes direct and indirect recognition algorithms and compares features of existing LPR systems.
2. A proposed LPR system is described that uses image preprocessing, license plate detection, character segmentation, and character recognition. Preprocessing improves image quality before detecting the license plate region.
3. The proposed system applies contour tracing and Canny edge detection algorithms to the license plate region to sharpen character edges for recognition.
Automatic Number Plate Recognition System (ANPR) A Survey.pdfTina Gabel
This document provides a survey of different approaches to Automatic Number Plate Recognition (ANPR) systems. It discusses the common steps in ANPR systems, which include vehicle image capture, number plate detection, character segmentation, and character recognition. It then reviews various techniques that have been used for number plate detection, such as image binarization, edge detection, Hough transform, blob detection, and connected component analysis. The document surveys several papers that have implemented different approaches for number plate detection, including sliding concentric windows, configurable methods, and feature-based localization. Finally, it discusses methods that have been used for character segmentation and recognition.
Similar to Indonesian license plate recognition with improved horizontal-vertical edge projection (20)
Development of depth map from stereo images using sum of absolute differences...nooriasukmaningtyas
This article proposes a framework for the depth map reconstruction using stereo images. Fundamentally, this map provides an important information which commonly used in essential applications such as autonomous vehicle navigation, drone’s navigation and 3D surface reconstruction. To develop an accurate depth map, the framework must be robust against the challenging regions of low texture, plain color and repetitive pattern on the input stereo image. The development of this map requires several stages which starts with matching cost calculation, cost aggregation, optimization and refinement stage. Hence, this work develops a framework with sum of absolute difference (SAD) and the combination of two edge preserving filters to increase the robustness against the challenging regions. The SAD convolves using block matching technique to increase the efficiency of matching process on the low texture and plain color regions. Moreover, two edge preserving filters will increase the accuracy on the repetitive pattern region. The results show that the proposed method is accurate and capable to work with the challenging regions. The results are provided by the Middlebury standard dataset. The framework is also efficiently and can be applied on the 3D surface reconstruction. Moreover, this work is greatly competitive with previously available methods.
Model predictive controller for a retrofitted heat exchanger temperature cont...nooriasukmaningtyas
This paper aims to demonstrate the practical aspects of process control theory for undergraduate students at the Department of Chemical Engineering at the University of Bahrain. Both, the ubiquitous proportional integral derivative (PID) as well as model predictive control (MPC) and their auxiliaries were designed and implemented in a real-time framework. The latter was realized through retrofitting an existing plate-and-frame heat exchanger unit that has been operated using an analog PID temperature controller. The upgraded control system consists of a personal computer (PC), low-cost signal conditioning circuit, national instruments USB 6008 data acquisition card, and LabVIEW software. LabVIEW control design and simulation modules were used to design and implement the PID and MPC controllers. The performance of the designed controllers was evaluated while controlling the outlet temperature of the retrofitted plate-and-frame heat exchanger. The distinguished feature of the MPC controller in handling input and output constraints was perceived in real-time. From a pedagogical point of view, realizing the theory of process control through practical implementation was substantial in enhancing the student’s learning and the instructor’s teaching experience.
Control of a servo-hydraulic system utilizing an extended wavelet functional ...nooriasukmaningtyas
Servo-hydraulic systems have been extensively employed in various industrial applications. However, these systems are characterized by their highly complex and nonlinear dynamics, which complicates the control design stage of such systems. In this paper, an extended wavelet functional link neural network (EWFLNN) is proposed to control the displacement response of the servo-hydraulic system. To optimize the controller's parameters, a recently developed optimization technique, which is called the modified sine cosine algorithm (M-SCA), is exploited as the training method. The proposed controller has achieved remarkable results in terms of tracking two different displacement signals and handling external disturbances. From a comparative study, the proposed EWFLNN controller has attained the best control precision compared with those of other controllers, namely, a proportional-integralderivative (PID) controller, an artificial neural network (ANN) controller, a wavelet neural network (WNN) controller, and the original wavelet functional link neural network (WFLNN) controller. Moreover, compared to the genetic algorithm (GA) and the original sine cosine algorithm (SCA), the M-SCA has shown better optimization results in finding the optimal values of the controller's parameters.
Decentralised optimal deployment of mobile underwater sensors for covering la...nooriasukmaningtyas
This paper presents the problem of sensing coverage of layers of the ocean in three dimensional underwater environments. We propose distributed control laws to drive mobile underwater sensors to optimally cover a given confined layer of the ocean. By applying this algorithm at first the mobile underwater sensors adjust their depth to the specified depth. Then, they make a triangular grid across a given area. Afterwards, they randomly move to spread across the given grid. These control laws only rely on local information also they are easily implemented and computationally effective as they use some easy consensus rules. The feature of exchanging information just among neighbouring mobile sensors keeps the information exchange minimum in the whole networks and makes this algorithm practicable option for undersea. The efficiency of the presented control laws is confirmed via mathematical proof and numerical simulations.
Evaluation quality of service for internet of things based on fuzzy logic: a ...nooriasukmaningtyas
The development of the internet of thing (IoT) technology has become a major concern in sustainability of quality of service (SQoS) in terms of efficiency, measurement, and evaluation of services, such as our smart home case study. Based on several ambiguous linguistic and standard criteria, this article deals with quality of service (QoS). We used fuzzy logic to select the most appropriate and efficient services. For this reason, we have introduced a new paradigmatic approach to assess QoS. In this regard, to measure SQoS, linguistic terms were collected for identification of ambiguous criteria. This paper collects the results of other work to compare the traditional assessment methods and techniques in IoT. It has been proven that the comparison that traditional valuation methods and techniques could not effectively deal with these metrics. Therefore, fuzzy logic is a worthy method to provide a good measure of QoS with ambiguous linguistic and criteria. The proposed model addresses with constantly being improved, all the main axes of the QoS for a smart home. The results obtained also indicate that the model with its fuzzy performance importance index (FPII) has efficiently evaluate the multiple services of SQoS.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Smart monitoring system using NodeMCU for maintenance of production machinesnooriasukmaningtyas
Maintenance is an activity that helps to reduce risk, increase productivity, improve quality, and minimize production costs. The necessity for maintenance actions will increase efficiency and enhance the safety and quality of products and processes. On getting these conditions, it is necessary to implement a monitoring system used to observe machines' conditions from time to time, especially the machine parts that often experience problems. This paper presents a low-cost intelligent monitoring system using NodeMCU to continuously monitor machine conditions and provide warnings in the case of machine failure. Not only does it provide alerts, but this monitoring system also generates historical data on machine conditions to the Google Cloud (Google Sheet), includes which machines were down, downtime, issues occurred, repairs made, and technician handling. The results obtained are machine operators do not need to lose a relatively long time to call the technician. Likewise, the technicians assisted in carrying out machine maintenance activities and online reports so that errors that often occur due to human error do not happen again. The system succeeded in reducing the technician-calling time and maintenance workreporting time up to 50%. The availability of online and real-time maintenance historical data will support further maintenance strategy.
Design and simulation of a software defined networkingenabled smart switch, f...nooriasukmaningtyas
Using sustainable energy is the future of our planet earth, this became not only economically efficient but also a necessity for the preservation of life on earth. Because of such necessity, smart grids became a very important issue to be researched. Many literatures discussed this topic and with the development of internet of things (IoT) and smart sensors, smart grids are developed even further. On the other hand, software defined networking is a technology that separates the control plane from the data plan of the network. It centralizes the management and the orchestration of the network tasks by using a network controller. The network controller is the heart of the SDN-enabled network, and it can control other networking devices using software defined networking (SDN) protocols such as OpenFlow. A smart switching mechanism called (SDN-smgrid-sw) for the smart grid will be modeled and controlled using SDN. We modeled the environment that interact with the sensors, for the sun and the wind elements. The Algorithm is modeled and programmed for smart efficient power sharing that is managed centrally and monitored using SDN controller. Also, all if the smart grid elements (power sources) are connected to the IP network using IoT protocols.
Efficient wireless power transmission to remote the sensor in restenosis coro...nooriasukmaningtyas
In this study, the researchers have proposed an alternative technique for designing an asymmetric 4 coil-resonance coupling module based on the series-to-parallel topology at 27 MHz industrial scientific medical (ISM) band to avoid the tissue damage, for the constant monitoring of the in-stent restenosis coronary artery. This design consisted of 2 components, i.e., the external part that included 3 planar coils that were placed outside the body and an internal helical coil (stent) that was implanted into the coronary artery in the human tissue. This technique considered the output power and the transfer efficiency of the overall system, coil geometry like the number of coils per turn, and coil size. The results indicated that this design showed an 82% efficiency in the air if the transmission distance was maintained as 20 mm, which allowed the wireless power supply system to monitor the pressure within the coronary artery when the implanted load resistance was 400 Ω.
Grid reactive voltage regulation and cost optimization for electric vehicle p...nooriasukmaningtyas
Expecting large electric vehicle (EV) usage in the future due to environmental issues, state subsidies, and incentives, the impact of EV charging on the power grid is required to be closely analyzed and studied for power quality, stability, and planning of infrastructure. When a large number of energy storage batteries are connected to the grid as a capacitive load the power factor of the power grid is inevitably reduced, causing power losses and voltage instability. In this work large-scale 18K EV charging model is implemented on IEEE 33 network. Optimization methods are described to search for the location of nodes that are affected most due to EV charging in terms of power losses and voltage instability of the network. Followed by optimized reactive power injection magnitude and time duration of reactive power at the identified nodes. It is shown that power losses are reduced and voltage stability is improved in the grid, which also complements the reduction in EV charging cost. The result will be useful for EV charging stations infrastructure planning, grid stabilization, and reducing EV charging costs.
Topology network effects for double synchronized switch harvesting circuit on...nooriasukmaningtyas
Energy extraction takes place using several different technologies, depending on the type of energy and how it is used. The objective of this paper is to study topology influence for a smart network based on piezoelectric materials using the double synchronized switch harvesting (DSSH). In this work, has been presented network topology for circuit DSSH (DSSH Standard, Independent DSSH, DSSH in parallel, mono DSSH, and DSSH in series). Using simulation-based on a structure with embedded piezoelectric system harvesters, then compare different topology of circuit DSSH for knowledge is how to connect the circuit DSSH together and how to implement accurately this circuit strategy for maximizing the total output power. The network topology DSSH extracted power a technique allows again up to in terms of maximal power output compared with network topology standard extracted at the resonant frequency. The simulation results show that by using the same input parameters the maximum efficiency for topology DSSH in parallel produces 120% more energy than topology DSSH-series. In addition, the energy harvesting by mono-DSSH is more than DSSH-series by 650% and it has exceeded DSSHind by 240%.
Improving the design of super-lift Luo converter using hybrid switching capac...nooriasukmaningtyas
In this article, an improvement to the positive output super-lift Luo converter (POSLC) has been proposed to get high gain at a low duty cycle. Also, reduce the stress on the switch and diodes, reduce the current through the inductors to reduce loss, and increase efficiency. Using a hybrid switch unit composed of four inductors and two capacitors it is replaced by the main inductor in the elementary circuit. It’s charged in parallel with the same input voltage and discharged in series. The output voltage is increased according to the number of components. The gain equation is modeled. The boundary condition between continuous conduction mode (CCM) and discontinuous conduction mode (DCM) has been derived. Passive components are designed to get high output voltage (8 times at D=0.5) and low ripple about (0.004). The circuit is simulated and analyzed using MATLAB/Simulink. Maximum power point tracker (MPPT) controls the converter to provide the most interest from solar energy.
Third harmonic current minimization using third harmonic blocking transformernooriasukmaningtyas
Zero sequence blocking transformers (ZSBTs) are used to suppress third harmonic currents in 3-phase systems. Three-phase systems where singlephase loading is present, there is every chance that the load is not balanced. If there is zero-sequence current due to unequal load current, then the ZSBT will impose high impedance and the supply voltage at the load end will be varied which is not desired. This paper presents Third harmonic blocking transformer (THBT) which suppresses only higher harmonic zero sequences. The constructional features using all windings in single-core and construction using three single-phase transformers explained. The paper discusses the constructional features, full details of circuit usage, design considerations, and simulation results for different supply and load conditions. A comparison of THBT with ZSBT is made with simulation results by considering four different cases
Power quality improvement of distribution systems asymmetry caused by power d...nooriasukmaningtyas
With an increase of non-linear load in today’s electrical power systems, the rate of power quality drops and the voltage source and frequency deteriorate if not properly compensated with an appropriate device. Filters are most common techniques that employed to overcome this problem and improving power quality. In this paper an improved optimization technique of filter applies to the power system is based on a particle swarm optimization with using artificial neural network technique applied to the unified power flow quality conditioner (PSO-ANN UPQC). Design particle swarm optimization and artificial neural network together result in a very high performance of flexible AC transmission lines (FACTs) controller and it implements to the system to compensate all types of power quality disturbances. This technique is very powerful for minimization of total harmonic distortion of source voltages and currents as a limit permitted by IEEE-519. The work creates a power system model in MATLAB/Simulink program to investigate our proposed optimization technique for improving control circuit of filters. The work also has measured all power quality disturbances of the electrical arc furnace of steel factory and suggests this technique of filter to improve the power quality.
Studies enhancement of transient stability by single machine infinite bus sys...nooriasukmaningtyas
Maintaining network synchronization is important to customer service. Low fluctuations cause voltage instability, non-synchronization in the power system or the problems in the electrical system disturbances, harmonics current and voltages inflation and contraction voltage. Proper tunning of the parameters of stabilizer is prime for validation of stabilizer. To overcome instability issues and get reinforcement found a lot of the techniques are developed to overcome instability problems and improve performance of power system. Genetic algorithm was applied to optimize parameters and suppress oscillation. The simulation of the robust composite capacitance system of an infinite single-machine bus was studied using MATLAB was used for optimization purpose. The critical time is an indication of the maximum possible time during which the error can pass in the system to obtain stability through the simulation. The effectiveness improvement has been shown in the system
Renewable energy based dynamic tariff system for domestic load managementnooriasukmaningtyas
To deal with the present power-scenario, this paper proposes a model of an advanced energy management system, which tries to achieve peak clipping, peak to average ratio reduction and cost reduction based on effective utilization of distributed generations. This helps to manage conventional loads based on flexible tariff system. The main contribution of this work is the development of three-part dynamic tariff system on the basis of time of utilizing power, available renewable energy sources (RES) and consumers’ load profile. This incorporates consumers’ choice to suitably select for either consuming power from conventional energy sources and/or renewable energy sources during peak or off-peak hours. To validate the efficiency of the proposed model we have comparatively evaluated the model performance with existing optimization techniques using genetic algorithm and particle swarm optimization. A new optimization technique, hybrid greedy particle swarm optimization has been proposed which is based on the two aforementioned techniques. It is found that the proposed model is superior with the improved tariff scheme when subjected to load management and consumers’ financial benefit. This work leads to maintain a healthy relationship between the utility sectors and the consumers, thereby making the existing grid more reliable, robust, flexible yet cost effective.
Energy harvesting maximization by integration of distributed generation based...nooriasukmaningtyas
The purpose of distributed generation systems (DGS) is to enhance the distribution system (DS) performance to be better known with its benefits in the power sector as installing distributed generation (DG) units into the DS can introduce economic, environmental and technical benefits. Those benefits can be obtained if the DG units' site and size is properly determined. The aim of this paper is studying and reviewing the effect of connecting DG units in the DS on transmission efficiency, reactive power loss and voltage deviation in addition to the economical point of view and considering the interest and inflation rate. Whale optimization algorithm (WOA) is introduced to find the best solution to the distributed generation penetration problem in the DS. The result of WOA is compared with the genetic algorithm (GA), particle swarm optimization (PSO), and grey wolf optimizer (GWO). The proposed solutions methodologies have been tested using MATLAB software on IEEE 33 standard bus system
Intelligent fault diagnosis for power distribution systemcomparative studiesnooriasukmaningtyas
Short circuit is one of the most popular types of permanent fault in power distribution system. Thus, fast and accuracy diagnosis of short circuit failure is very important so that the power system works more effectively. In this paper, a newly enhanced support vector machine (SVM) classifier has been investigated to identify ten short-circuit fault types, including single line-toground faults (XG, YG, ZG), line-to-line faults (XY, XZ, YZ), double lineto-ground faults (XYG, XZG, YZG) and three-line faults (XYZ). The performance of this enhanced SVM model has been improved by using three different versions of particle swarm optimization (PSO), namely: classical PSO (C-PSO), time varying acceleration coefficients PSO (T-PSO) and constriction factor PSO (K-PSO). Further, utilizing pseudo-random binary sequence (PRBS)-based time domain reflectometry (TDR) method allows to obtain a reliable dataset for SVM classifier. The experimental results performed on a two-branch distribution line show the most optimal variant of PSO for short fault diagnosis.
A deep learning approach based on stochastic gradient descent and least absol...nooriasukmaningtyas
More than eighty-five to ninety percentage of the diabetic patients are affected with diabetic retinopathy (DR) which is an eye disorder that leads to blindness. The computational techniques can support to detect the DR by using the retinal images. However, it is hard to measure the DR with the raw retinal image. This paper proposes an effective method for identification of DR from the retinal images. In this research work, initially the Weiner filter is used for preprocessing the raw retinal image. Then the preprocessed image is segmented using fuzzy c-mean technique. Then from the segmented image, the features are extracted using grey level co-occurrence matrix (GLCM). After extracting the fundus image, the feature selection is performed stochastic gradient descent, and least absolute shrinkage and selection operator (LASSO) for accurate identification during the classification process. Then the inception v3-convolutional neural network (IV3-CNN) model is used in the classification process to classify the image as DR image or non-DR image. By applying the proposed method, the classification performance of IV3-CNN model in identifying DR is studied. Using the proposed method, the DR is identified with the accuracy of about 95%, and the processed retinal image is identified as mild DR.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Accident detection system project report.pdfKamal Acharya
The Rapid growth of technology and infrastructure has made our lives easier. The
advent of technology has also increased the traffic hazards and the road accidents take place
frequently which causes huge loss of life and property because of the poor emergency facilities.
Many lives could have been saved if emergency service could get accident information and
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Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
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SENTIMENT ANALYSIS ON PPT AND Project template_.pptx
Indonesian license plate recognition with improved horizontal-vertical edge projection
1. Indonesian Journal of Electrical Engineering and Computer Science
Vol. 21, No. 2, February 2021, pp. 811~821
ISSN: 2502-4752, DOI: 10.11591/ijeecs.v21.i2.pp811-821 811
Journal homepage: http://ijeecs.iaescore.com
Indonesian license plate recognition with improved
horizontal-vertical edge projection
Ida Nurhaida, Imam Nududdin, Desi Ramayanti
Faculty of Computer Science, Universitas Mercu Buana, Indonesia
Article Info ABSTRACT
Article history:
Received Feb 26, 2020
Revised Apr 7, 2020
Accepted May 6, 2020
License plate recognition (LPR) is one of the classical problems in the
field of object recognition. Its application is very crucial in the
automation of transportation system since it helps to recognise a vehicle
identity, which information is stored in the license plate. LPR usually
consists of three major phases: pre-processing, license plate localisation,
optical character recognition (OCR). Despite being classical, its
implementation faced with much more complicated problems in the real
scenario. This paper proposed an improved LPR algorithm based on
modified horizontal-vertical edge projection. The method uses for
detecting and localising the region of interest. It is done using the
horizontal and vertical projection of the image. Related works proved
that the modified horizontal-vertical edge projection is the simplest
method to be implemented, yet very effective against Indonesian license
plate. However, its performance gets reduced when specular reflection
occurs in the sample image. Therefore, morphological operations are
utilised in the pre-processing phase to reduce such effects while
preserving the needed information. Eighty sample images which
captured using various camera configurations were used in this research.
Based on the experimental results, our proposed algorithm shows an
improvement compared with the previous study and successfully detect
71 license plates in 80 image samples which results in 88.75% accuracy.
Keywords:
Computer vision
Image processing
License plate recognition
Morphology operation
This is an open access article under the CC BY-SA license.
Corresponding Author:
Ida Nurhaida
Faculty of Computer Science
Universitas Mercu Buana
Jl. Meruya Selatan no. 1, Indonesia
Email: ida.nurhaida@mercubuana.ac.id
1. INTRODUCTION
Object recognition is one of the most interesting fields among the computer vision researcher since
it's applicable in many sectors of our everyday life. One of the classical problem that still relevant even today
was license plate recognition, because of the many challenges that must be tackled such as variations of the
license plate characteristics in each country around the world, noise in the image, environmental factors, etc.
Many researchers around the world have developed many algorithms for license plate recognition. Still, due
to the limited sample, most of them are crafted only for a specific country with specific license plate
characteristics (background colour, format, font type, etc.). They might not work for other countries with
different license plate characteristics.
License plate in Indonesia has its unique characteristics. It has a black coloured background with a
white border and text. The usage of unique alphabet code to differentiate license plates across the regions in
Indonesia, also the license plate is structured based on a standardised format. The details are described within
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the Figure 1. License plate recognition usually consists of three major phases: pre-processing, license plate
localisation, and optical character recognition (OCR). Pre-processing is a phase where the input image will
be prepared, enhanced, and optimised for further processing [1, 2]. According to [3-5], license plate
localisation is a phase in which the vehicle license plate area is to be determined, localised, and segmented
out of the original picture to isolate the license plate from the other objects which considered as noise. After
the license plate has been segmented and isolated, the next phase will be character recognition, where all of
the characters within the license plate will be extracted and processed by some sort of algorithm, usually
template matching.
Figure 1. Indonesian license plate standardised format
The study [3, 4] modified the algorithm to adapt to Indonesian license plate characteristics, and it
approves to be effective. Those papers have reviewed and summarised techniques for license plate
recognition in Indonesia throughout recent years. The papers consist of a short description of the reviewed
algorithms and also the comparison between each algorithm. Despite being effective, the algorithm still
struggles when it faced with specular reflection or object with high saturation intensities. Hidayatullah, et al.,
[3] introduce an algorithm that is heavily influenced by [5] but with a better approach to adapt to the
Indonesian license plate. It shows that the algorithm is pretty useful in detecting Indonesian license plate with
63 detected license plate out of 80 sample images. The researcher also stated that one of the key problems
which cause the detection to be a failure is specular reflection. Devapriya et al., [5] proposed a license plate
recognition algorithm for the Indian license plate that rely heavily on the morphological operation. Another
approach of license plate recognition is presented by Gohil [6]. Research [6] proposed a simple yet effective
algorithm to be used for Californian license plate recognition. The algorithm works by detecting the area of
vehicle license plate based on the horizontal edge and vertical edge projection. It is the most straightforward
yet very useful algorithm for license plate recognition. However, this is not very simple to implement this
algorithm for the Indonesian license plate. It is hard to expose such rectangular object because Indonesian
license plate has a black background, and sometimes the white border is not visible due to the lighting
condition or get obstructed by another object. Therefore, it will be very prone to misdetection. We believe
something could be done to improve the performance of the algorithm.
2. RELATED WORKS
Various research for license plate recognition has been introduced using many algorithms.
According to the paper, it seems like many researchers have been utilising morphology operation and edge
detection technique to construct their license plate recognition algorithm. Moreover, some researcher even
combined it with Machine Learning to help improve recognition accuracy [7, 8].
We could not ignore the fact that morphology operation is one of the most popular techniques to be used in
object recognition [9, 10]. The reason for such popularity is that morphology operation while being not very
complicated and costly; it could expose the whole structure of an object to ease the recognition of the desired
object or to detect an undesired object to be removed from the image. Another essential technique is edge
detection. Edge detection aims at finding the changes in brightness in an image to be used for capturing the
critical event [11, 12]. The two of them are usually combined [13]. Although, some researchers have
developed an object recognition algorithm using an edge detection and comparable using morphology
operation [14, 15]. This algorithm only works effectively when the border of the "object to be recognised" is
strongly defined.
Another simplified approach to be used for object recognition is by using SIFT (scale-invariant
feature transform), which proved to be robust against the change of scale and orientation [16]. It is known as
the algorithm for object recognition. The algorithm works by utilising features in an image, known as critical
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points, to determine whether an object is similar or not. However, the algorithm may not be going to be very
useful because of the lack of textures and features in the Indonesian license plate.
Following the research, [17] conducted a study to implement the algorithm for the Indonesian license plate.
The algorithm still works effectively with some modification, especially in the pre-processing phase against
various Indonesian license plate. Further research has been conducted by [18, 19]. Research [18] resulted that
the implemented algorithm can recognise the characters on the police plate number in the average percentage
79,43%. K-Nearest Neighbors algorithm reads all these characters. The test results show that this vehicle
plate recognition system provides 83,33% success rate from 30 test data.
On the other hand, Damayanti et al. [19] using different approaches for the recognition of the license
plate. The research utilises feature area and K-Nearest Neighbor (KNN) as a classification method. In
summary, these results show that accuracy of 99.44%.
3. PROPOSED METHOD
In this research, we proposed an improved Horizontal-Vertical Edge Projection algorithm. The pre-
processing method proposed in this study has an unusual behaviour because it could reduce the intensity of
specular reflection by a significant amount while highlighting the interest point, which is the vehicle license
plate. Thus it could be very useful to settle the problems. The complete flow of the proposed algorithm could
be seen in Figure 2.
Figure 2. Flowchart of the proposed algorithm
3.1. Pre-processing
Pre-Processing is the phase to enhance the input image and remove unwanted data, noises, and
distortion from the input image. The purpose of image preprocessing is to eliminate irrelevant information in
the image, restore real useful information, enhance the detectability of the relevant information, and simplify
the data to the maximum, thereby improving the reliability of image feature extraction and recognition.
Common methods are: (1) grayscale; (2) geometric transformation; (3) image enhancement. The goal of this
phase is to emphasise useful image information while reducing unwanted data. Furthermore, the data
collected have many error or noise, so that it needs to perform the data preprocessing operation [20, 21].
3.1.1. RGB to grayscale conversion
The first step in the pre-processing phase is to convert the input image into the grayscale format.
The reason behind this conversion is to simplify the computation process in the next stage. The most
common technique to convert the RGB image into grayscale is to use the Luminosity method represented in
(1) below. The result of this operation can be seen in Figure 3.
(1)
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(a) (b)
Figure 3. Comparison between, (a) original image and (b) grayscaled image
3.1.2. Opening morphology operation
The next step is to perform opening morphology operation to the image. Opening morphology could
be achieved by implementing the erosion operation first, followed by the dilation operation using the same
structuring element for both operations [22]. The information retrieved after this step is useful to detect
problematic regions caused by having too much contrast/intensity or regions with specular reflection, which
could confuse the license plate localisation algorithm. Opening operation is described by (2).
(2)
where represents erosion operation while represents dilation operation. The outcome of this operation
is visualised by Figure .
Figure 4. The result of opening morphology
3.1.3. Top-hat transform
Top-hat transform is one of the most used operations for image enhancement [22, 23]. In this case,
the input image will be the grayscaled image, and the opening counterpart is the outcome of the previous
step, which holds information about problematic regions as described before. The problematic regions will no
longer dominate the image, and its intensity will be reduced to a significant amount. The region of interest
(around the vehicle license plate) will be enhanced. This enhancement is what we needed to increase the
accuracy of Horizontal-Vertical Edge Projection method in detecting the vehicle license plate. Figure 5
visualises the pre-processing phase proposed by Devapriya [5] and the proposed algorithm.
(a) (b)
Figure 5. Comparison of pre-processing phase, (a) pre-processing as proposed by [3],
(b) the proposed algorithm
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3.2. License plate localisation
After the enhanced image has been retrieved from the pre-processing phase, it is now the time to
detect and localise the vehicle license plate. It means that the vehicle license plate should be identified
precisely and isolated from other objects in the image.
3.2.1. Vertical edge projection
The enhanced image will be scanned vertically, and the sum of all magnitude differences will be
calculated column-wise for every row of the image. All of the obtained value after the calculation will then
be projected into a histogram representation for the analysis process. Figure 6 visualised the sample image
and the histogram projection along the vertical edge.
The histogram should be smoothed using the low pass filter in order to normalise such drastic
changes. The resulting values will be averaged to get a threshold level. The elimination process will yield to
segmentation of the image to several rows based on the projection peaks. Figure 7 shows the comparison
between each histogram projection for each operation.
Figure 6. Vertical edge projection calculation
(a)
(b)
(c)
Figure 1. Vertical edge processing visualisation, (a) rough histogram projection, (b) low pass filter,
(c) histogram projection thresholding
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3.2.2. Region of interest extraction based on the vertical projection
In this step, all the remaining peaks in the projection histogram will be analysed to find the region of
interest. Since Indonesian license plate has a black coloured background with white coloured text and border,
it must yield a significant amount of magnitude in the projection histogram. Therefore, the region of interest
should be the peaks which contains a global maxima across the projection, and any other peaks should be
eliminated from the image to localise the region of interest see Figure 8.
Figure 2. ROI based on the vertical edge
3.2.3. Horizontal edge projection
After the region of interest has been localised along the vertical edge, analysis along the horizontal
edge is executed to precisely detect the vehicle license plate. The procedure to calculate the horizontal edge
projection is identically the same as before, except the sum of all magnitude differences will be calculated
between every neighbouring pixel in the vertical axis within such column. The outcome for each process is
visualised by Figure 9.
(a) (b) (c)
Figure 9. Horizontal edge processing visualisation, (a) rough histogram projection, (b) low pass filter,
(c) histogram projection thresholding
3.2.4. Region of interest extraction based on horizontal projection
Different than before, the region of interest is analysed through the width of each peak in the
projection histogram. We define a threshold value to filter out the peaks based on its width. For every peak
having a width less than the threshold value will be considered as noise and must be eliminated, while those
above the threshold value will be kept. In this research, we define the threshold value as 25 units see Figure
10.
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Figure 3. Final ROI
3.2.5. License plate segmentation
After the license plate has been precisely located and noises are successfully removed, the
grayscaled image in the first phase will be cropped based on the region of interest defined from all of the
previous steps see Figure 11. This cropped image will then be passed for the character recognition phase.
Figure 4. Cropped license plate
3.3. Character recognition
For character recognition phase, we modify the font template as recommended by Hidayatullah [4].
Instead, we use our own modified font template. This difference may affect the character recognition process.
3.3.1. Thresholding (Otsu’s method)
The first step in character recognition phase is to convert the input image into a binary image by
thresholding. The most commonly used method for thresholding is Otsu's method since it provides a dynamic
threshold level which calculated from the image pixels [24, 25]. This conversion means that for every pixel
value below the threshold level will be converted into 0 (the darkest colour). In contrast, every pixel value
above the threshold level will be converted into 1 (the brightest colour). The outcome of this conversion
usually called binary image since it contains only 0s and 1s value in the pixel magnitude, as shown in Figure 12.
(a) (b)
Figure 12. Comparison between, (a) grayscaled image and (b) binary image
3.3.2. Character candidate extraction
After the image has been converted into a binary image, we could quickly identify and extract all of
the objects within the image through Connected Component Analysis. All of the detected objects are
considered as character candidate as shown in Figure 13, however, some of them are better considered as
noise. Therefore, further noise removal should be executed. Fortunately, Connected Component Analysis not
only detect objects within an image but also assign some properties to the object such as centroids, and
bounding box [26]. Enhancing the quality of binary image for plate image can be done based on preprocessed
to remove the noise and compensate poor illumination [27]. Then, we can identify the position and the size of
an object which could be useful for the noise removal process.
Noise removal process consists of several stages. The first stage is applying a median filter to
remove salt and pepper noise. The second stage is eliminating objects whose size is below the defined
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threshold acquired from the equation proposed by [4]. The third stage is to eliminate objects which length
exceed 1/8 length of the image. Furthermore, the last step is to check whether an object is parallel to the other
object or not since the characters on a license plate are usually nearly parallel. After all of the license plate
character candidates has been truly determined, they will be cropped out as an individual character. After
that, they will be normalised by resizing them into 42x24 pixels to match the size of the font template. This
cropping and normalisation process is required to perform template matching.
Figure 5. The visualisation of character candidate detection
3.3.3. Template matching
In this step, every individual character will be matched against sets of character template through
such calculation to find similarity between them. In this research, we used the normalised cross-correlation
method. Every calculation results obtained from the operation will be mapped inside an array for the voting
process. The character is recognised as such string through the voting process by finding in which template
the operation achieved maximum correlation value.
4. EXPERIMENTAL RESULTS
4.1. License plate localisation
To conduct a fair comparison between [3] and the proposed algorithm, we perform the research
using the same sample images as used by them with their permission. The sample consists of 80 images,
captured using various camera configurations such as the camera distance (between 1m, 1.5m, or 2m), and
height (between 55cm, 1m, or 2m). Our proposed algorithm has successfully detected the vehicle license
plate in 71 samples out of 80 samples, while [3] detect 63 samples out of 80 samples. Below is the table of
comparison between [3] and the proposed algorithm.
Figure 14 presents the visualisation of the successful detection process for some samples, where the
condition is not very optimal. Even though the proposed algorithm shows an improvement to the detection
accuracy, it still finds struggles to detect the vehicle license plate in some samples. Some failed detection are
shown in Figure 15. Figure 15 (a,b) shows an extreme case where the detected license plate area exceeded the
actual vehicle license plate due to the failure of the pre-processing phase to reduce the overly saturated car.
This condition caused high projection value in the histogram and confused the analysis process. Figure 15 (c)
shows the failure of the detection due to the usage of non-standardised license plate. The thinner font will not
produce significant peaks in the projection histogram. Therefore, parts of the license plate did not exceed the
threshold value in the horizontal projection analysis and get eliminated from the region of interest.
Sample Image Pre-Processing Segmentation Result
a
b
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Figure 14. (a) Camera a little higher than license plate, (b) case with specular reflection
Sample Image Pre-Processing Segmentation Result
a
b
c
Figure 15. (a, b) license plate area exceeded, (c) license plate area cut
4.2. Character recognition
All of the 71 successfully detected license plates were then used for further processing which is the
character recognition phase. Jaccard Similarity Index is used to calculate the similarity between the
recognised text and the actual license plate. From the experiment, as shown in Table 1, we achieved an
average of 77.97% similarity, while [3] obtained an average of 85.87% similarity. This gap might be caused
by the presence of noise which falsely recognised as one of the license plate characters or the distortion
among the license plate characters, which implies to false recognition. Table 2 shows the results of the
character recognition stage.
Table 1. The comparison results in detection accuracy
Algorithm Detected sample (Out of 80 sample) %
Modified Horizontal-Vertical Projection 63 78.75%
Proposed Agorithm 71 88.75%
Table 2. The results of character recognition
N
o.
Detection Result Actual License Plate Recognised Similarity (%)
1
.
D811CJ D811CJ 100
2
.
D403LL D403LL 100
3
.
D1732WG D173G 71.43
4
.
D1685XT 1X 28.57
5
.
D1311GW - 0
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5. CONCLUSION
The performance of Horizontal-Vertical Edge Projection could be improved by modifying the pre-
processing phase to enhance the input image. Horizontal-Vertical Edge Projection is the simplest yet
effective algorithm for license plate detection. Still, it suffers from the specular reflection problem because it
analyses the region of interest-based on the magnitude value projection. Therefore, any object with high
saturation intensity or specular reflection from the vehicle itself could confuse the analysis process, which
often results in misdetection. Our research proposed an algorithm with the implementation to reduce such
problems by doing an image enhancement in the pre-processing phase, which in result reducing the effect of
specular reflection and object with high saturation intensity while preserving and enhancing the region of
interest information. Our proposed algorithm has proven to be effective and improved the performance by a
significant amount, from 63 (78.75%) detected samples into 71 (88.75%). The pre-processing phase
sometimes still failed to reduce the specular reflection effect. For enhancement, sample image quality should
also be taken care, blurry image and a dirty car could fail the localisation and recognition process. Another
concern is that this algorithm is not very robust against orientation changes, and due to the limited sample we
could not test the algorithm against the case when the camera is not perpendicular to the license plate.
REFERENCES
[1] S. Chakraborty and R. Parekh, “An Improved Template Matching Algorithm for Car License Plate Recognition,”
International Journal of Computer Applications, vol. 118, no. 25, pp. 16-22, 2015.
[2] Suzhi Zhang, Yuhong Wu, Jun Chang, “Survey of Image Recognition Algorithms”, IEEE 4th
Information
Technology, Networking, Electronic and Automation Control Conference, 2020.
[3] Sergio Montazzolli Silva, Claudio Rosito Jung, “Real-time license plate detection and recognition using deep
convolutional neural networks”, Journal of Visual Communication and Image Representation, 2020.
[4] P. Hidayatullah, F. Feirizal, H. Permana, Q. Mauluddiah, and A. Dwitama, “License plate detection and recognition
for Indonesian cars,” International Journal on Electrical Engineering Informatics, vol. 8, no. 2, pp. 331-346, 2016.
[5] W. Devapriya, C. N. K. Babu, and T. Srihari, “Indian License Plate Detection and Recognition Using
Morphological Operation and Template Matching,” International Journal of Computer, Electrical, Automation,
Control and Information Engineering, vol. 9, no. 4, pp. 1049-1055, 2015.
[6] Naikur B. Gohil, “Car License Plate Detection”, Sacramento, California State University, 2010.
[7] Hui Li, Peng Wang, and Chunhua Shen, “Toward End-to-End Car License Plate Detection and Recognition With
Deep Neural Networks”, IEEE Transactions On Intelligent Transportation Systems, pp 99, 2017.
[8] Naaman Omar, Abdulkadir Sengur, Salim Ganim Saeed Al-Ali, “Cascaded deep learning-based efficient approach
for license plate detection and recognition”, Expert Systems With Applications, 2020.
[9] Zhongli Wang, Xiping Ma and Wenlin Huang, “Vehicle License Plate Recognition Based on Wavelet Transform and
Vertical Edge Matching”, International Journal of Pattern Recognition and Artificial Intelligence, vol. 34, no. 6, 2020.
[10] K. Adi, A. P. Widodo, C. E. Widodo, A. Pamungkas, and A. B. Putranto, “Automatic vehicle counting using
background subtraction method on gray scale images and morphology operation,” Journal of Physics: Conference
Series, vol. 1025, no. 1, 2018.
[11] Elena Medvedeva, Igor Trubin, and Pavel Kasper, “Vehicle License Plate Recognition Based on Edge Detection”,
Proceeding of the 26th
Conference of Fruct Association, 2020.
[12] S. Mahalakshmi and P. M. Karani, “Study of Edge Detection Techniques in Automatic License Plate Recognition
(ALPR),” International Research Journal of Engineering and Technology, vol. 4, no. 5, pp. 1658-1661, 2017.
[13] S. Borker, “Implementation of Vehicle License Plate Recognition Using Canny Edge Detection,” International
Journal of Computer Science and Engineering Technology (IJCSET), vol. 6, no. 06, pp. 376-379, 2015.
[14] Chen, Y, Luo, S., “Research on License Plate Location in Different Scenes”, International Conference on Artificial
Intelligence and Advanced Manufacturing, 2019.
[15] Nurhaida, A. Noviyanto, R. Manurung, and A. M. Arymurthy, “Automatic Indonesian’s Batik Pattern Recognition
Using SIFT Approach,” Procedia Computer Science, vol. 59, pp. 567-576, 2015.
[16] F. A. Hermawati and R. Koesdijarto, “a Real-Time License Plate Detection System for Parking Access,”
TELKOMNIKA (Telecommunication, Computing, Electronics and Control), vol. 8, no. 2, pp. 97-106, 2010.
[17] Rahmat, B., Joelianto, E., Ketut Eddy Purnama, Purnomo, M.H., “Vehicle license plate image segmentation system using
cellular neural network optimized by adaptive fuzzy and Neuro-Fuzzy algorithms”, International Journal of Multimedia
and Ubiquitous Engineering, vol. 11, no. 12, pp. 383-400, 2016.
[18] Fitri Damayanti, Sri Herawati, Imamah, Fifin Ayu M, Aeri Rachmad, “Indonesian license plate recognition based
on area feature extraction”, TELKOMNIKA (Telecommunication, Computing, Electronics and Control), vol.17, no.
2, pp. 620-627, 2019.
[19] V. Ayumi, L. M. R. Rere, M. I. Fanany, and A. M. Arymurthy, “Random Adjustment - Based Chaotic
Metaheuristic Algorithms for Image Contrast Enhancement,” Journal of a Science and Information, vol. 10, no. 2,
pp. 67-76, 2017.
[20] S. Salazar-Colores, J. M. Ramos-Arreguín, C. J. Ortiz Echeverri, E. Cabal-Yepez, J. C. Pedraza-Ortega, and J.
Rodriguez-Resendiz, “Image dehazing using morphological opening, dilation and Gaussian filtering,” Signal,
Image Video Process., vol. 12, no. 7, pp. 1329-1335, 2018.
11. Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752
Indonesian license plate recognition with improved horizontal-vertical edge projection (Ida Nurhaida)
821
[21] Mujiono Sadikin, Fahri Alfiandi, “Comparative Study of Classification Method on Customer Candidate Data to
Predict its Potential Risk”, International Journal of Electrical and Computer Engineering (IJECE), vol. 8, no. 6,
pp. 4763-4771, 2018.
[22] J. C. M. Roman, H. L. Ayala, and J. L. V. Noguera, “Top-Hat Transform for Enhancement of Aerial Thermal
Images,” 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Niteroi, pp. 277-284,
2017.
[23] J. C. Mello Román, J. Luis Vázquez Noguera, H. Legal-Ayala, L. Moré, and D. P. Pinto-Roa, “Image enhancement
with preservation of brightness and details using multiscale top-hat transform,” 2019 XXII Symposium on Image,
Signal Processing and Artificial Vision (STSIVA), Bucaramanga, Colombia, pp. 1-5, 2019.
[24] Feng, Y., Zhao, Li, X., Zhang, X., Li, H., “A multi-scale 3D Otsu thresholding algorithm for medical image
segmentation”, Digital Signal Processing: A Review Journal, vol. 60, pp. 186-199, 2017.
[25] Mohamed Abd El Aziz, Ahmed A. Ewees, and Aboul Ella Hassanien, “Whale Optimization Algorithm and Moth-
Flame Optimization for Multilevel Thresholding Image Segmentation”, Expert Systems With Applications, vol. 83,
pp. 242-256, 2017.
[26] L. He, X. Ren, Q. Gao, X. Zhao, B. Yao, and Y. Chao, “The connected-component labelling problem: A review of
state-of-the-art algorithms,” Pattern Recognition, vol. 70, pp. 25-43, 2017.
[27] Sajed Rakhshani, Esmat Rashedi, Hossein Nezamabadi, “Representation learning in a deep network for license plate
recognition”, Multimedia Tools and Applications, vol. 79, no. 19-20, pp. 13267-13289, 2020.
BIOGRAPHIES OF AUTHORS
Ida Nurhaida was born in Kuantan (Malaysia) in 1971. She is a researcher and member of
Faculty of Computer Science Universitas Mercu Buana, Indonesia. Her areas of interest and
research are in the image processing, pattern recognition and image retrieval system which she
formalised in her PhD (2010) on this subject in the Faculty of Computer Science, University of
Indonesia. She has presented papers at conferences both home and abroad, published articles and
papers in various journals, and contributed a chapter to the book.
Imam Nududdin was born in Jakarta (Indonesia) in 1998. He received his bachelor degree in
Computer Science from Universitas Mercu Buana, Indonesia in 2020. His research interest are
image processing, pattern recognition, and computer vision.
Desi Ramayanti was born in Padang (Indonesia) in 1981. She is a researcher and member of
Faculty of Computer Science Universitas Mercu Buana, Indonesia. Her areas of interest and
research are in machine learning, networking and software development which she formalised in
her Master (2008) on this subject in Computer Engineering Institute of Technology Bandung.