Real Time Automatic Number Plate Recognition Using Morphological Algorithm

The rising increase of up to date urban and national road networks over the last three decades become known the need of capable monitoring and management of road traffic. Expected techniques for traffic measurements, such as inductive loops, sensors or EM microwave detectors, endure from sober shortcomings, luxurious to install, they demand traffic distraction during installation or maintenance, they are massive and they are unable to notice slow or momentary stop vehicles. On the divergent, systems that are based on video are simple to install, use the existing infrastructure of traffic observation. Currently most reliable method is through the detection of number plates, i.e., automatic number plate recognition (ANPR), which is also branded as automatic license plate recognition (ALPR), or radio frequency transponders. The first revalent step of information is finding of moving objects in video streams and background subtraction is a very accepted approach for foreground segmentation. Next step is License plate extraction which is an essential stage in license plate recognition for automatic transport system. We are planned for two ways for removal of license plates and comparing it with other existing methods. The Extracted license plates are segmented into particular characters by means of a region-based manner. The recognition scheme unites adaptive iterative thresholding with a template matching algorithm. The method is strong to illumination, character size and thickness, skew and small character breaks. The main reward of this system is its real-time capability and that it does not require any extra sensor input (e.g. from infrared sensors) except a video stream. This system is judged on a huge number of vehicle images and videos. The system is also computationally extremely efficient and it is appropriate for others related image recognition applications. This system has broad choice of applications such as access control, ringing, border patrol, traffic control, finding stolen cars, etc. Furthermore, this technology does not need any fitting on cars, such as transmitter or responder

PENGENALAN KARAKTER PADA PLAT NOMOR KENDARAAN BERBASIS SUPPORT VECTOR MACHINE

Pengenalan  plat nomor kendaraan merupakan salah satu teknik penting sebagai bagian dari sistem transportasi cerdas yang dapat digunakan untuk mengidentifikasi kendaraan hanya dengan memahami plat nomor. Dalam banyak penelitian pengenalan plat nomor kendaraan dibagi mejadi tiga area penelitian diantaranya license plate detection, license plate segmentation dan license plate character recognition. Metode neural network telah digunakan untuk mengenali plat nomor kendaraan namun metode ini memiliki masalah overfitting jika memiliki dataset dalam ukuran yang besar disamping itu metode neural network  sering terjebak dalam local optimum. Support vector maching tidak mengalami masalah overfitting karena metode ini hanya melakukan training satu kali namun support vector machine memiliki kelemahan dalam menentukan parameter kernel yang terbaik untuk mencapai global optimum, tujuan dari penelitian ini adalah menentukan parameter sigma (ó) dan parameter pinalti (C) terbaik untuk pengenalan karakter dengan metode support vector machine.Metode penelitian dalam penelitian ini dibagi menjadi dua bagian yakni metode pada proses pelatihan (training) dan proses pengenalan (recognition), dalam proses pengenalan tahap yang dilakukan meliputi tahap preprocessing dengan metode binerisasi citra,  segmentasi dengan metode connected component labeling, pada tahap ekstraksi fitur dilakukan pemetaan citra dalam matriks 5x5 dan pada tahap klasifikasi di terapkan menggunakan metode support vector machine dengan pengujian dan pengukuran akurasinya menggunakan confusion matrix. Dari hasil percobaan dengan menggunakan 100 sample citra plat nomor yang di ujikan, hasil pengukuran menunjukkan bahwa parameter ó=0.8 dan C=15 merupakan nilai parameter terbaik untuk penelitian ini dengan overall accuracy sebesar 91%.   Kata Kunci : Support Vector Machine,  Support Vector, pengenalan  karakter, plat nomo

Heuristics for license plate localization and hardware implementation of Automatic License Plate Recognition (ALPR) system

The project “Heuristics for license plate localization and hardware implementation of Automatic License Plate Recognition (ALPR) system” deals with detection and recognition of license plate from a captured front view of any car. The work follows all the steps in an ALPR system like preprocessing, segmentation, and license plate identification, extraction of individual characters and finally recognition of each character to form a string to match with the registered License plate numbers. The main contribution in the work is to expedite the number plate isolation from a set of segmented candidates. It utilizes a set of heuristics typically transition from object to background and vice-versa, aspect ratio of the bounding boxes. This narrow down the number of candidates for further processing and further, we suggest a rank based identification of each character in the number plate. The process scheme along with the existing methodologies is integrated to develop the overall ALPR system. A set of standard images collected from internet as well as self-collected car images of staff vehicles are used for simulation. The experiments are conducted using OpenCV. For validation, a working ALPR hardware prototype is developed using AVR development board (ATmega32 microcontroller), GP2D120 distance measurement sensor (IR-sensor).Interfacing between PC and controller-board is done using serial port. The model works with an accuracy of 80%. The ALPR system has a further scope to improve the recognition speed using parallel processing of various sub-steps

Automatic Vehicle Detection, Tracking and Recognition of License Plate in Real Time Videos

Automatic video analysis from traffic surveillance cameras is a fast-emerging field based on computer vision techniques. It is a key technology to public safety, intelligent transport system (ITS) and for efficient management of traffic. In recent years, there has been an increased scope for automatic analysis of traffic activity. We define video analytics as computer-vision-based surveillance algorithms and systems to extract contextual information from video. In traffic scenarios several monitoring objectives can be supported by the application of computer vision and pattern recognition techniques, including the detection of traffic violations (e.g., illegal turns and one-way streets) and the identification of road users (e.g., vehicles, motorbikes, and pedestrians). Currently most reliable approach is through the recognition of number plates, i.e., automatic number plate recognition (ANPR), which is also known as automatic license plate recognition (ALPR), or radio frequency transponders. Here full-featured automatic system for vehicle detection, tracking and license plate recognition is presented. This system has many applications in pattern recognition and machine vision and they ranges from complex security systems to common areas and from parking admission to urban traffic control. This system has complex characteristics due to diverse effects as fog, rain, shadows, uneven illumination conditions, occlusion, variable distances, velocity of car, scene's angle in frame, rotation of plate, number of vehicles in the scene and others. The main objective of this work is to show a system that solves the practical problem of car identification for real scenes. All steps of the process, from video acquisition to optical character recognition are considered to achieve an automatic identification of plates