scholarly journals Detection of Non-Helmet Riders and Extraction of License Plate Number using Yolo v2 and OCR Method

2019 ◽  
Vol 9 (2) ◽  
pp. 5167-5172
Keyword(s):  
Object Detection ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Detection System ◽  
Research Work ◽  
License Plate ◽  
Plate Number ◽  
Traffic Violations ◽  
Registration Number ◽  
Traffic Violation

In current situation, we come across various problems in traffic regulations in India which can be solved with different ideas. Riding motorcycle/mopeds without wearing helmet is a traffic violation which has resulted in increase in number of accidents and deaths in India. Existing system monitors the traffic violations primarily through CCTV recordings, where the traffic police have to look into the frame where the traffic violation is happening, zoom into the license plate in case rider is not wearing helmet. But this requires lot of manpower and time as the traffic violations frequently and the number of people using motorcycles is increasing day-by-day. What if there is a system, which would automatically look for traffic violation of not wearing helmet while riding motorcycle/moped and if so, would automatically extract the vehicles’ license plate number. Recent research have successfully done this work based on CNN, R-CNN, LBP, HoG, HaaR features,etc. But these works are limited with respect to efficiency, accuracy or the speed with which object detection and classification is done. In this research work, a Non-Helmet Rider detection system is built which attempts to satisfy the automation of detecting the traffic violation of not wearing helmet and extracting the vehicles’ license plate number. The main principle involved is Object Detection using Deep Learning at three levels. The objects detected are person, motorcycle/moped at first level using YOLOv2, helmet at second level using YOLOv3, License plate at the last level using YOLOv2. Then the license plate registration number is extracted using OCR (Optical Character Recognition). All these techniques are subjected to predefined conditions and constraints, especially the license plate number extraction part. Since, this work takes video as its input, the speed of execution is crucial. We have used above said methodologies to build a holistic system for both helmet detection and license plate number extraction.

scholarly journals An Illegal Dumping Detection System Based on Image Processing in OpenALPR

2021 ◽  
Vol 15 ◽  
pp. 1-7
Author(s):  
Wan Zakiah Wan Ismail
Keyword(s):  
Real Time ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Detection System ◽  
Social Economy ◽  
Raspberry Pi ◽  
License Plate ◽  
Microwave Radar ◽  
Plate Number ◽  
Illegal Dumping

Tipping or depositing large waste onto land using unauthorized and unlicensed methods are considered as illegal dumping. The increasing rate of illegal dumping becomes a crucial nation issue because this activity causes negative impacts to social, economy and environment. Thus, study on detecting the dumping activities is conducted to control the illegal dumping activities in Malaysia. Raspberry Pi with Python language is used as the microprocessor and a Raspberry Pi camera module with a microwave radar sensor are interfaced to it to capture the image of any vehicles entering the illegal dumping site. The image is captured to recognize the license plate of the vehicle. The method in this study is by using Open Automatic License Plate Recognition (ALPR), Open Computer Vision (CV) libraries and Optical Character Recognition (OCR) to detect the character of the plate registration number. The outcome of the study consists of recognition of Malaysia vehicles’ plate number and the automatic real time email notification on the illegal dumping case. The detection system can be used for case monitoring since the plate number recognition is done in real time. The system can be upgraded to ensure its sustainability in the harsh and isolated environment.

scholarly journals Vehicle Number Plate Recognition using Micro Processor

2019 ◽  
Vol 8 (12) ◽  
pp. 3682-3684
Keyword(s):  
Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition System ◽  
Processing Technology ◽  
License Plate ◽  
Text Format ◽  
Optical Character ◽  
Plate Number ◽  
Image Processing Technology

An automatic license number plate recognition system that uses image processing technology for identifying the written characters and numbers on the vehicle’ license plate. The system can be used in highly secured areas to provide more safety, and can be used in parking, traffic, and other places to monitor all vehicle’s number plate in a predefined area. The character is recognized by the OCR technology that is optical character recognition system. It generates the vehicle’s license plate number in a text format. The recognized number from the license plate then can be used to retrieve more information about the vehicle and the owner.

scholarly journals Automatic Number Plate Recognition Using Image Processing

2021 ◽  
Vol 9 (VII) ◽  
pp. 2394-2400
Author(s):  
Hrithik Roshan Palampatla
Keyword(s):  
Machine Learning ◽  
Character Recognition ◽  
Surveillance System ◽  
Optical Character Recognition ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
License Plate ◽  
Digital Cameras ◽  
Registration Number ◽  
The Individual

Automatic Number Plate Recognition (ANPR) is a mass surveillance system that captures the image of vehicles and recognizes their registration number issued by government. ANPR is often used in the detection of stolen vehicles, traffic surveillance system. Our project presents a model in which the vehicle license plate image is obtained by the digital cameras and the image is processed to get the number plate information. A vehicle image is captured and processed using various methods. Vehicle number plate region is extracted using the deep neural networks. Optical character recognition is implemented using certain machine learning algorithms for the character recognition. The system is implemented using deep neural network model, machine learning algorithms and is simulated in python, and its performance is tested on real images. It is observed that the developed model successfully detects the license plate region and recognizes the individual characters. There are various recognition strategies that have been produced and number plate recognition systems are today used in different movement and security applications, such as access and border control, parking, or tracking of stolen vehicles.

scholarly journals Aplikasi Pendeteksi Plat Nomor Kendaraan Berbasis Raspberry Pi Menggunakan Website Untuk Pelanggaran Lalu Lintas

2019 ◽  
Vol 11 (2) ◽  
pp. 84-89
Author(s):  
Siska Aulia ◽  
Popy Maria ◽  
Ramiati Ramiati
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Raspberry Pi ◽  
License Plate ◽  
Long Distance ◽  
The Public ◽  
Segmentation Methods ◽  
Traffic Violations ◽  
Motorized Vehicles ◽  
Wheeled Vehicles

Motorized vehicles in Indonesia consist of two-wheeled vehicles and four-wheeled vehicles. The number of motorized vehicles is increasing every year. The higher the vehicle volume the higher the level of traffic violations. Every violator will be charged a ticket by the ticketing officer if the vehicle user does not obey the driving rules. The ticketing process in Indonesia is still manually using paper by writing violations committed by violators on a piece of paper. This article is an attempt to make it easier for the public and police in traffic violations. This article is designed for vehicle license plate detection applications and traffic violation websites. The plate identification process begins with taking a plate image through a Raspberry Pi-based camera or webcam. The plate image results using the Raspberry Pi camera are carried out by processing the vehicle plate digital image by segmentation methods and Optical Character Recognition (OCR) using matlab. The vehicle plate character results obtained are used as input to identify traffic violations. The form of traffic violations can be seen on the traffic ticket website. Based on the results of OCR testing proved to be able to recognize the image of the vehicle plate. Raspberry Pi based camera for long distance or wireless communication. The results from the traffic ticket website are used as evidence to process motorists who have violated traffic.  

scholarly journals Artificial Intelligence Software Application for Contactless Traffic Violation Apprehension in the Philippines

2021 ◽  
Vol 25 (4) ◽  
pp. 410-415
Author(s):  
John Anthony C. Jose ◽  
◽  
Ciprian D. Billones Jr. ◽  
Allysa Kate M. Brillantes ◽  
Robert Kerwin C. Billones ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Character Recognition ◽  
Web Application ◽  
Optical Character Recognition ◽  
The Philippines ◽  
License Plate ◽  
Law Enforcement Agencies ◽  
Software Application ◽  
Traffic Violation ◽  
Processing Techniques

This paper presents a prototype of a centralized contactless traffic violation apprehension system composed of an artificial intelligence (AI) engine and a web application. The AI engine collects traffic data, primarily traffic violation data, through a contactless approach by using different video and image processing techniques and AI algorithms in its three modules: license plate detection, optical character recognition (OCR), and number coding violation detection. The web application consolidates all the data produced by the AI engine and provides a graphical user interface (GUI) for data management, visualization, and analysis. This contactless apprehension system aims to automate, standardize, and streamline the existing processes of law enforcement agencies and institutions for a more efficient apprehension of traffic violators and help them improve their traffic planning and management in the congested areas of the Philippines.

ConvNet-Based Optical Recognition for Engineering Drawings

2017 ◽  
Author(s):  
Andrew Brock ◽  
Theodore Lim ◽  
J. M. Ritchie ◽  
Nick Weston
Keyword(s):  
Object Detection ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Cross Validation ◽  
Convolutional Networks ◽  
Optical Character ◽  
Engineering Drawings ◽  
Machine Analysis ◽  
Fold Cross Validation ◽  
Optical Recognition

End-to-end machine analysis of engineering document drawings requires a reliable and precise vision frontend capable of localizing and classifying various characters in context. We develop an object detection framework, based on convolutional networks, designed specifically for optical character recognition in engineering drawings. Our approach enables classification and localization on a 10-fold cross-validation of an internal dataset for which other techniques prove unsuitable.

scholarly journals Residential Area Surveillance and Parking System

2020 ◽  
Vol 9 (5) ◽  
pp. 602-606
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Residential Area ◽  
High Rate ◽  
Identification System ◽  
License Plate ◽  
License Plate Recognition ◽  
Position Effects ◽  
Plain Text ◽  
Alert Message

The vehicles playing the vital role in our day to day life for transport, and some of the vehicles violates the traffic rules are also increasing, vehicle theft, unnecessary entering into highly restricted areas, increased number of accidents lead to increase in the rate of crime slowly. The vehicle had its own identity it should be recognized which plays the major role in the world. For recognition of the vehicles which are used commonly in the field of safety and security system, LPDR plays a major role and the vehicle registration number is recognized at some certain distance accurately. License Plate recognition is the most efficient and cost effective technique used for detection and recognition purposes. Automatic license plate recognition (ALPR) is used for finding the location of the license plate in the vehicle. These methods and techniques vary based on the conditions like, quality of the image, vehicle on a fine-tuned position, effects of lighting, type of image, etc. The objective is to design an efficient automatic conveyance identification system of sanctioned or unauthorized in the residential societies by utilizing the conveyance number plate. By getting the car image from the surveillance camera in the entrance, we recognizing the number plate and the characters are extracted using OCR (optical character recognition). It converts the character in the image to plain text. Then the plain text of the license plate is cross-verified with the database to check whether the vehicle belongs to residents or visitor. It sends the alert message to the security official when a new visitor request method in a residential area. The log details are stored separately for the resident and visitor in the database. It also provides the details about the parking area availability in the residential area. By calculating the number of vehicles in and out of the area, the detail or availability parking slot is displayed and it sis robust to the size, lighting effects with high rate of detection.

scholarly journals Intelligent Parking System

2019 ◽  
Vol 8 (2S3) ◽  
pp. 550-553
Keyword(s):  
Urban Communities ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Exit Time ◽  
Region Of Interest ◽  
License Plate ◽  
Past Study ◽  
Entry Time ◽  
Parking System ◽  
Best Fit

As a key part of Automated vehicle technology Intelligent Parking System has become a popular research topic. Intelligent Parking System can grant permission to access the parking area with less human inference. This system can capture image of the vehicle, identify the type of vehicle and allot best fit and optimal parking slot based on its size. It extracts the vehicle’s License plate number, entry time, exit time and calculate total time of the vehicle present with in the parking space. Here, sensors are utilized to identify the presence of the vehicle during entry and exit. Two cameras are utilized to extract features. One camera is used to identify the Region of Interest, Vehicle license plate and identify the characters from the license plate. Tesseract Engine and Optical Character Recognition (OCR) functions are used to detect characters from the image. Another camera is utilized to extract features like dimensions of the vehicle using machine learning operations such as Convolutional Neural Network (CNN). Based on the size of the vehicle, best fit parking slot is allotted which gives optimal usage of parking area. These days the quantity of vehicles is expanding exceptionally, so that, searching for an empty parking slot turns out to be increasingly troublesome. By installing the Intelligent Parking System, in places like, shopping malls, train stations, and airports the need for searching of parking slot significantly reduces. A past study has demonstrated that traffic because of vehicle’s parking slot searching in downtowns of significant urban communities can represent half of the absolute traffic. With such a hefty traffic jam and time delay in parking slot identifying, Intelligent Parking System will be in great demand

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