Number Plate Extraction Research Articles

Comprehensive study on the development of an automatic helmet violator detection system (AHVDS) using advanced machine learning techniques

The number of motorcycle accidents has increased rapidly due to different causes. Helmet is a one of the major safety equipment that helps to reduce the fatal accidents even though many of the motorcyclists are not used that. Currently many researchers have focused on traffic violator detection but they are yet to identify the violated person details as it requires high resolution cameras and expensive sensors for number plate extraction. This research article is focused on comprehensive study on the development of an automated helmet violation detection with number plate extraction in real time. This study divides the dataset into rider with helmet, rider without helmet, passenger with helmet and passenger without helmet classes, and four training model was created to detect helmet in rider and passengers. The proposed model was constructed in three stages to attain high accuracy with a vast amount of training dataset. In the first stage, the helmet was detected in the input image using the deep learning object-detection model .In the second stage, if the helmet identification confirmed in the input image then the entire motorcyclist image is segmented based on object segmentation model. In the third stage, number plate training model is deployed into the same object detection and object segmentation model to identify and segment the license plates in the motorcyclist image. This study primarily incorporates the Mask Region based Convolution Neural Networks (Mask R-CNN) for object detection and segmentation Further, it incorporates the three different deep neural networks like Dense block Network (DenseNet-121), Residual Network (ResNet-101) and Inception Residual Network architecture V2layers (Inception Res Net V2) to create accurate helmet violator detections with more extensive information. The experimental results indicates that this comprehensive helmet violator detection study improves the helmet detection accuracy by 6.2 %.In addition, the above three comprehensive proposed models are evaluated under Wilcoxon rank test for better sensitive information. Further, the less object detection loss (2 %) and significant computational time variation (69 %) indicates the performance of the comprehensive helmet violator detection system was good compared to existing study.

Number Plate Detection and Parking Allotment

Abstract: In present day scenario the security and authentication is very much needed to make a safety world. Beside all security one vital issue is recognition of number plate from the car for Authorization. In the busy world everything cannot be monitor by a human, so automatic license plate recognition is one of the best application for authorization without involvement of human power. In the proposed method we have make the problem into three fold, firstly extraction of number plate region, secondly segmentation of character and finally Authorization through recognition and classification. For number plate extraction and segmentation, we have used morphological based approaches whereas for classification we have used Neural Network as classifier. The proposed method is working well in varieties of scenario and the performance level is quite good.

Helmet and Triple Seat Detection with Number Plate Extraction for Motorcyclists using Advanced Deep Learning Techniques

The main aim of this project is to store the info fata securely on the cloud storage, by splitting the information in small different chunks of data and storing those parts of data on cloud in such a way that preserves data integrity, confidentiality and ensures availability. The use of cloud computing is increasing in so many organizations and Information technology industries are providing new software’s with low cost. Cloud computing is helpful in terms of low cost and accessibility of information. Cloud computing provides lot of features with low cost and of knowledge accessibility by using Internet. To ensure that data is protected, cloud computing plays a major role, as the users usually store their important information on the cloud, and these providers are also unknown and untrusted. So, the most difficult issue is to share the data in secure way while preserving that information from any untrusted cloud. this approach ensures that protection and privacy of users important information by storing the client’s data on any single cloud storage.

AUTOMATIC NUMBER PLATE RECOGNITION SYSTEM

Automatic Number Plate Recognition (ANPR) is an image-processing technology and an important field of research that identifies vehicles by their number plates in which the number plate information is extracted from vehicle's image or from sequence of images without direct human intervention. ANPR consists of 4 phases: - Preprocessing, number plate extraction, character segmentation, character recognition. This paper presents an efficient approach for number plate extraction from preprocessed vehicle input image using morphological operations, thresholding, sobel vertical edge detection and connected component analysis. The input image is firstly preprocessed using iterative bilateral filter and adaptive histogram equalization.

An efficient automated vehicle license plate recognition system under image processing

<span lang="EN-US">An automated vehicle license plate recognition system using image processing techniques identifies vehicle numbers without human interference. This system has significant impact because of its good application in various fields like car parking, access control, speed control, automatic toll collection, border security, traffic violence detection and surveillance applications. This paper presents a methodology that is quite simple but at the same time very much efficient and this system consists of four sequential modules which are preprocessing, number plate extraction, number plate character segmentation and character recognition. Preprocessing aims to improve the image quality that is captured in various illumination conditions and stick out outstanding information that we need, which is favorable to subsequent processing including extraction, segmentation and recognition. After preprocessing various morphological operations are applied to extract the desired license plate region. Then for segmentation the bounding box method is applied that segments each letter and number present on the license plate region. Finally, template matching is applied in identifying all segmented characters present in the license plate image. The experimental results showed that the proposed system can recognize license plate characters efficiently with higher accuracy. Using MATLAB software, the proposed method attains recognition accuracy of 94.17%.</span>

License Plate Recognition

<p>License Plate Recognition (LPR) system is an application of computer vision and image processing technology that takes video of vehicles and take the vehicle frame as input image and by extracting their number plate from whole vehicle image, it displays the number plate information into text. The overall accuracy and efficiency of whole LPR system depends on number plate extraction phase as character segmentation and character recognition phases are also depend on the output of this phase. Higher be the quality of captured input vehicle image more will be the chances of proper extraction of vehicle number plate area. The approach used to segment the image is bilateral filtering algorithm and canny edge detection algorithm. Then we predict the license plate from processed image using py–tesseract OCR and match the retrieved text which is vehicle number plate with database. Finally we get the details of the particular vehicle from the database.</p>

Real Time License Plate Recognition from Video Streams using Deep Learning

With ever increasing number of vehicles, vehicular management is one of the major challenges faced by urban areas. Automation in terms of detecting vehicle license plate using real time automatic license plate recognition (RT-ALPR) approach can have many use cases in automated defaulter detection, car parking and toll management. It is a computationally complex task that has been addressed in this work using a deep learning approach. As compared to previous approaches, license plates have been recognized from full camera stills as well as parking videos with noise. On a dataset of 4800 car images, the accuracy obtained is 91% on number plate extraction from images, 93% on character recognition. Proposed ALPR system has also been applied to vehicle videos shot at parking exits. Overall 85% accuracy was obtained in real-time license number recognition from these videos.

A Broad Survey on Performance Analysis of Number Plate Recognition from Stationary Images and Video Sequences

Licensed Number plate recognition plays vital role in smart cities for maintaining Law & Order and traffic management. NPR based system mainly involves four stages namely 1) Image capture & Pre-Processing 2) Number plate area determination 3) Character Segmentations 4) Recognition of all character. This survey paper extensively analyzed the method of extraction of number plate, its platform, performance and execution time. With the development of Multilayer Perceptron Network accuracy and time in image processing has been achieved up to a great instant. Hence this analysis will help the precise assessment in establishing research and enable developers to assess which strategies are aggressive in present environment.

License Plate Recognition Using Undecimated Wavelet Transform

<p>License Plate Recognition (LPR) is the mission of identifying the vehicle using number plate extraction. An efficient method for recognizing plate based on Undecimated Wavelet Transform (UWT) is proposed. Plates are recognized using features from undecimated coefficients in this system. Morphological edge detection technique is used to get accurate results after feature extraction. Finally detected images are used for classification purpose using the feature coefficients. This technique is applied to all the unidentified and training images, extracted features are used as input to Back Propagation Neural Network (BPNN). The evaluation of the system is based on public database.</p>

Automatic Car Number Plate Recognition System for Authorization

In present day scenario the security and authentication is very much needed to make a safety world. Beside all security one vital issue is recognition of number plate from the car for Authorization. In the busy world everything cannot be monitor by a human, so automatic license plate recognition is one of the best application for authorization without involvement of human power. In the proposed method we have make the problem into three fold, firstly extraction of number plate region, secondly segmentation of character and finally Authorization through recognition and classification. For number plate extraction and segmentation we have used morphological based approaches where as for classification we have used Neural Network as classifier. The proposed method is working well in varieties of scenario and the performance level is quiet good.

Performance and Analysis of Automatic License Plate Localization and Recognition from Video Sequences

Abstract The works presents license plate recognition system using connected component analysis and template matching model for accurate identification. Automatic license plate recognition (ALPR) is the extraction of vehicle license plate information from an image. The system model uses already captured images for this recognition process. First the recognition system starts with character identification based on number plate extraction, Splitting characters and template matching. ALPR as a real life application has to quickly and successfully process license plates under different environmental conditions, such as indoors, outdoors, day or night time. It plays an important role in numerous real-life applications, such as automatic toll collection, traffic law enforcement, parking lot access control, and road traffic monitoring. The system uses different templates for identifying the characters from input image. After character recognition, an identified group of characters will be compared with database number plates for authentication. The proposed model has low complexity and less time consuming interms of number plate segmentation and character recognition. This can improve the system performance and make the system more efficient by taking relevant sample.

An Automatic Number Plate Recognition System under Image Processing

Automatic Number Plate Recognition system is an application of computer vision and image processing technology that takes photograph of vehicles as input image and by extracting their number plate from whole vehicle image , it display the number plate information into text. Mainly the ANPR system consists of 4 phases: Acquisition of Vehicle Image and PreProcessing, Extraction of Number Plate Area, Character Segmentation and Character Recognition. The overall accuracy and efficiency of whole ANPR system depends on number plate extraction phase as character segmentation and character recognition phases are also depend on the output of this phase. Further the accuracy of Number Plate Extraction phase depends on the quality of captured vehicle image. Higher be the quality of captured input vehicle image more will be the chances of proper extraction of vehicle number plate area. The existing methods of ANPR works well for dark and bright/light categories image but it does not work well for Low Contrast, Blurred and Noisy images and the detection of exact number plate area by using the existing ANPR approach is not successful even after applying existing filtering and enhancement technique for these types of images. Due to wrong extraction of number plate area, the character segmentation and character recognition are also not successful in this case by using the existing method. To overcome these drawbacks I proposed an efficient approach for ANPR in which the input vehicle image is pre-processed firstly by iterative bilateral filtering , adaptive histogram equalization and number plate is extracted from pre-processed vehicle image using morphological operations, image subtraction, image binarization/thresholding, sobel vertical edge detection and by boundary box analysis. Sometimes the extracted plate area also contains noise, bolts, frames etc. So the extracted plate area is enhanced by using morphological operations to improve the quality of extracted plate so that the segmentation phase gives more successful output. The character segmentation is done by connected component analysis and boundary box analysis and finally in the last character recognition phase, the characters are recognized by matching with the template database using correlation and output results are displayed. This approach works well for low contrast, blurred, noisy as well as for dark and light/bright category images. The comparison is done between the ANPR with Adaptive Histogram Equalization and Iterative Bilateral Filtering that is the proposed approach and the existing ANPR approach using metrics: MSE, PSNR and Success rate.

AUTOMATIC NUMBER PLATE RECOGNITION SYSTEM FOR VEHICLE IDENTIFICATION

The system is designed as the authorized automatic vehicle identifier, for the security of the highly restricted areas. The number plate recognition using the image identification technology is implemented here. Now the technology has developing day by day. Almost in all the human needs fulfillment leads to the generation of new technology. This system can be implemented for the security checking of the highly restricted areas like defense military areas, parliament areas etc. The image of the vehicle is captured by using a camera and processed by the MATLAB. MATLAB is used for the image identification and the number plate extraction. The system contains an embedded section which controls the opening of the security gate and the communication purpose. The LCD screen provided at the entry gate gives visual assistance to the driver, whether he is not permitted at this area or permitted. The entry of the unauthorized vehicle is restricted and the message is send to the security cabin. The communication between MATLAB and the microcontroller is done with zigbee. By incorporating the RTO department database we can have full access to the vehicle’s owner and details of the vehicle. The technology which are adopted in this project can be utilized not only for the security but also in other fields such as parking fee collection, vehicle owner identification, etc. The project consists of an embedded section and image identification section. The image identification is done through the MATLAB and the embedded section is programmed using embedded C. Apart from the implementation of the system using RF-ID ,this Project have to be upgraded for the higher security levels. Image identification is the new technology which is adopted in this project. Keywordscomponents; image identification, number plate extraction, GSM, Zigbee, LCD Display --------------------------------------------------------------------***------------------------------------------------------------------

An Automatic Number Plate Recognition of Bangladeshi Vehicles

a-day, the importance of automatic traffic monitoring has drawn the attention to the intelligent transport systems. A number plate is the unique identification of a vehicle. Automatic Number Plate Recognition (ANPR) is designed to locate and recognize the number plate of a moving vehicle automatically. For Bangladeshi Vehicles there are no fixed rules for number plates like other countries. Because of the variety of conditions and patterns, the recognition process of number plates for Bangladeshi vehicles is so difficult. We develop a ANPR system which is divided into three part such as number plate Localization, Extraction and Recognition. After successfully complete localization and extraction of number plate we send that portion to a Bangla OCR to fetch the text of the number plate for further processing. In this paper we present a complete ANPR system for Bangladesh Vehicles using standard algorithms with some customization and with our own algorithm for Number Plate extraction which can detect Number Plate region and successfully extract detected region and then able to recognize that number plate text. KeywordsSobel Operator; Outsu threshold; Hough transform; Bangladeshi vehicles number plate

An Efficient Method of Number Plate Extraction from Indian Vehicles Image

Automatic Number Plate Recognition (ANPR) is an imageprocessing technology that identifies vehicles by their number plates without direct human intervention. It is an application of computer vision and important area of research due to its many applications. The main process of ANPR is divided into four stages. This paper presents a simple and efficient method for the extraction of number plate from the vehicle image based on morphological operations, thresholding and sobel edge detection, and the connected component analysis.

An Efficient Approach for Automatic Number Plate Recognition System under Image Processing

Automatic Number Plate Recognition (ANPR) system is an image processing technology that identifies the vehicles by tracking their number plate without direct human intervention and an application of computer vision. ANPR is an important method used in Intelligent Transportation System (ITS). The four phases of the ANPR system: Image acquisition and pre-processing, number plate extraction, character segmentation and character recognition are discussed in this paper. Number Plate Extraction is most crucial step in the ANPR system which affects the overall accuracy and efficiency of whole ANPR system. The quality of acquired vehicle image is a major factor in the success of ANPR. In this paper we have proposed an efficient approach for ANPR in which the input vehicle image is firstly pre-processed using iterative bilateral filter and adaptive histogram equalization and number plate is extracted from pre-processed vehicle image using morphological operations, image subtraction, thresholding, sobel edge detection and boundary box analysis. We compared the result of our proposed approach with existing method of ANPR. Keywords - Automatic number plate recognition, histogram equalization, adaptive histogram equalization, iterative bilateral filter, median filter, number plate extraction, morphological operations, image subtraction, thresholding, sobel edge detection, connected component analysis, boundary box analysis, character segmentation, character normalization, template matching, character recognition.

An Efficient way of Number Plate Alphabets and Numbers Extraction for Security Purpose

Extraction of number plate plays a key role in the recognition of vehicles. With the advancement in technology and increase in number of vehicles it is the need of the day to recognize registered and unregistered vehicles. For this purpose, in this paper we propose a system to automatically extract digits and alphabets from the number plate of vehicle and search it in the database for its registration. The proposed approach involves four different processes that include Image smoothing, Edge detection, Image segmentation and data extraction. The result shows that the proposed approach can easily detect and extract data from the number plates. Some of the number plates and its extracted data are shown in this paper. The results can further be implemented on automatic extraction of data from shields, sign etc. These two steps play an important role in the License Plate Recognition of a vehicle. There are many methods/algorithms developed for License Plate recognition. (8) and (7) extract the License Plate based on vertical edges using Hough Transform. (9) extract the colour using Neural Network and discriminate character using template matching. (10) Extracted the number plate based on genetic algorithm segmentation.(11) uses optical character recognition techniques for number plate recognition.(12) identified automatic vehicle using template matching and knowledge-guided boundary following. (13) Proposed a technique for extracting characters without preceding information of their location and range in the image. (14) Proposed a method for extracting Korean license plate on the basis of the colour of the plate. (6) Proposed feature based approach for the localization of number plate and its characters. Since number plate standards changes from place to place (3-6) therefore in this paper we propose a fresh based approach for accurately extracting data of same pixel value from the number plate. The proposed approach is independent of the digits and character variation but depends upon the analysis of the number of connected components. This paper is based on the analysis of connected components, containing four major steps that include, RGB to gray-scale conversion, Image smoothing, Edge detection, Image segmentation and data extraction. As RGB to gray-scale conversion is the key step in every image processing algorithm because it is easier to deal with 1 pixel value compare to 3 pixel values at one point and at the end it give us the same result.