Automatic Number Plate Recognition System for Vehicle Identification Using Optical Character Recognition

Qadri, Muhammad Yasir; Asif, Muhammad

doi:10.1109/icetc.2009.54

Cited by 174 publications

(92 citation statements)

References 1 publication

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“…ANPR used a sequence of image manipulation methods to detect, normalize and improve a license plate image and optical character recognition (OCR) to recognize the alphanumeric of license plate. During the last decade, it became much interesting alongside due to the enhancement of digital camera technology and computational processing [4]. It also can be used to detect and prevent criminal activities and parking control on a restiction parking area [8].…”

Section: Automatic Number Plate Recognitionmentioning

confidence: 99%

“…It consists of a camera to detect the number plate object and processing unit to process and extract the characters and interpret the pixels into numerically readable characters [3]. It became much exciting in the last decade along with the improvement of digital camera technology and the computational processing [4]. Nowadays, the ANPR system has been used in traffic law enforcement, including speed trap, stolen car detection and border monitoring.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design of Automatic Number Plate Recognition on Android Smartphone Platform

Gunawan

Mutholib²,

Kartiwi

2017

IJEECS

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Section: Automatic Number Plate Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design of Automatic Number Plate Recognition on Android Smartphone Platform

Gunawan

Mutholib²,

Kartiwi

2017

IJEECS

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“…These include inductive loop sensors [Sreedevi, 2005], which are buried in the road surface and count the vehicles driving over them; Automatic Number Plate Recognition cameras [Qadri and Asif, 2009] that can detect and re-identify passing vehicles; and microwave detectors [Wood et al, 2006] that detect the presence of waiting vehicles and pedestrians at stop lines and crossings. Now this set of traditional sensors is being augmented by the addition of new "wireless" data sources.…”

Section: Motivationmentioning

confidence: 99%

Fine-grained traffic state estimation and visualisation

Box¹,

Chen²,

Blainey³

et al. 2014

Proceedings of the Institution of Civil Engineers - Civil Engin

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Tools for visualizing the current traffic state are used by local authorities for strategic monitoring of the traffic network and by everyday users for planning their journey. Popular visualizations include those provided by Google maps and by Inrix. Both employ a "traffic light" colour coding system where roads on a map are coloured green if traffic is flowing normally and red or black if there is congestion.New sensor technology, especially from wireless sources (smartphones, 802.11p, Bluetooth), is enabling traffic state estimation with higher fidelity allowing the state on the road to be resolved down to the lane level.A case study is reported in which a traffic micro-simulation test bed is used to generate high fidelity state estimates. An interactive visualization of the fine grained traffic state is presented. The visualization is demonstrated using Google Earth and affords the user a detailed 3D view of the traffic state down to the lane level. Traffic states are also updated and streamed into the visualization in real time at 0.1 Hz so that traffic dynamics can be observed.

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“…Lettoumeau et al [13] presented an approach making it possible for an autonomous mobile robot to read characters, using characters printed on a colored sheet and a neural network trained to identify characters in different conditions to take into consideration the various viewpoints possible. Qadri et al [14] proposed an image processing technology which used the number license plate to identify the vehicle, called Automatic Number Plate Recognition (ANPR). The ANPR system robustly detects and recognizes the vehicle using the license plate under different lighting conditions, and it can be implemented at the entrance of highly restricted areas.…”

Section: Introductionmentioning

confidence: 99%

Visual Recognition and Its Application to Robot Arm Control

2015

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This paper presents an application of optical word recognition and fuzzy control to a smartphone automatic test system. The system consists of a robot arm and two webcams. After the words from the control panel that represent commands are recognized by the robot system, the robot arm performs the corresponding actions to test the smartphone. One of the webcams is utilized to capture commands on the screen of the control panel, the other to recognize the words on the screen of the tested smartphone. The method of image processing is based on the Red-Green-Blue (RGB) and Hue-Saturation-Luminance (HSL) color spaces to reduce the influence of light. Fuzzy theory is used in the robot arm's position control. The Optical Character Recognition (OCR) technique is applied to the word recognition, and the recognition results are then checked by a dictionary process to increase the recognition accuracy. The camera which is used to recognize the tested smartphone also provides object coordinates to the fuzzy controller, then the robot arm moves to the desired positions and presses the desired buttons. The proposed control scheme allows the robot arm to perform different assigned test functions successfully.

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Automatic Number Plate Recognition System for Vehicle Identification Using Optical Character Recognition

Cited by 174 publications

References 1 publication

Design of Automatic Number Plate Recognition on Android Smartphone Platform

Design of Automatic Number Plate Recognition on Android Smartphone Platform

Fine-grained traffic state estimation and visualisation

Visual Recognition and Its Application to Robot Arm Control

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