scholarly journals Fusion Methods of License Plate Detection and Super Resolution for Improving License Plate Recognition

2011 ◽  
Vol 16 (4) ◽  
pp. 53-60 ◽  
Author(s):  
Tae-Yup Song ◽  
Young-Hyun Lee ◽  
Min-Jae Kim ◽  
Bon-Hwa Ku ◽  
Han-Seok Ko
Keyword(s):  
Super Resolution ◽  
License Plate ◽  
License Plate Recognition ◽  
Fusion Methods ◽  
License Plate Detection

License Plate Detection and Facial Analysis Using TensorFlow Deep Learning Algorithm

2019 ◽  
Vol 16 (8) ◽  
pp. 3246-3251
Author(s):  
P. Manoj Prakash ◽  
Sreerag Premanathan ◽  
ShivamKumar Surwase ◽  
M. S. Antony Vigil ◽  
Shivam Bohare
Keyword(s):  
Learning Algorithm ◽  
Ccd Camera ◽  
License Plate ◽  
License Plate Recognition ◽  
Facial Analysis ◽  
Deep Learning Algorithm ◽  
Canny Edge ◽  
License Plate Detection ◽  
Toll Collection ◽  
Improved Technology

Nowadays license plate recognition has been applied in car access control, toll collection and other applications. Even though they exist, car thefts and illegal use of other proprietor’s license plate remain a problem. To deal with this, a computational programmed controlled framework is being developed. Also, facial analysis algorithm is implemented so as to create awareness among the common public. The way forward is to use an improved technology combination of License Plate Detection and Facial Analysis using artificial intelligence, in which vehicle image is captured by high resolution CCD camera and the license plate region is determined by image processing algorithms and facial analysis is done by using FaceNet and TensorFlow. The characters of the license plate is separated by segmentation and processed using the Canny Edge and Blob Coloring algorithm and the facial analysis is done using Facenet of TensorFlow.

scholarly journals IRAQI LICENSE PLATE RECOGNITION BASED ON MACHINE LEARNING

2020 ◽  
Vol 3 (4) ◽  
pp. 1-10
Author(s):  
Dunya A. Abd Alhamza ◽  
Ammar D. Alaythawy
Keyword(s):  
Machine Learning ◽  
Traffic Control ◽  
Character Recognition ◽  
Training Data ◽  
Correct Prediction ◽  
License Plate ◽  
License Plate Recognition ◽  
K Nearest Neighbors ◽  
License Plate Detection ◽  
Detection Search

 The license plate recognition (LPR) is an important system. LPR is helpful in many ranges such as private or public entrance, parking lots, traffic control and theft surveillance. This paper, offers (LPR) consist of four main stages (preprocessing, license plate detection, segmentation, character recognition) the first stage takes a photo by the camera then preprocessing in this image. License plate detection search for matching of license plate in the image to crop the correct plate. Segmentation performed by divide the numbers separately. The last stage is number recognition by using KNN (K- nearest neighbors) is one of the simple algorithms of machine learning used for matching numbers with training data to provide a correct prediction. The system was implemented using python3.5, open-cv library and shows accuracy performance result equal to 90% by using 50 images.

scholarly journals Robust Korean License Plate Recognition Based on Deep Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4140
Author(s):  
Hanxiang Wang ◽  
Yanfen Li ◽  
L.-Minh Dang ◽  
Hyeonjoon Moon
Keyword(s):  
Synthetic Data ◽  
Super Resolution ◽  
Real Data ◽  
Vital Role ◽  
License Plate ◽  
License Plate Recognition ◽  
Single Character ◽  
New Type ◽  
The Government ◽  
Processing Techniques

With the rapid rise of private vehicles around the world, License Plate Recognition (LPR) plays a vital role in supporting the government to manage vehicles effectively. However, an introduction of new types of license plate (LP) or slight changes in the LP format can break previous LPR systems, as they fail to recognize the LP. Moreover, the LPR system is extremely sensitive to the conditions of the surrounding environment. Thus, this paper introduces a novel deep learning-based Korean LPR system that can effectively deal with existing challenges. The main contributions of this study include (1) a robust LPR system with the integration of three pre-processing techniques (defogging, low-light enhancement, and super-resolution) that can effectively recognize the LP under various conditions, (2) the establishment of two original Korean LPR approaches for different scenarios, including whole license plate recognition (W-LPR) and single-character license plate recognition (SC-LPR), and (3) the introduction of two Korean LPR datasets (synthetic data and real data) involving a new type of LP introduced by the Korean government. Through several experiments, the proposed LPR framework achieved the highest recognition accuracy of 98.94%.

scholarly journals Vehicle License Plate Detection for Parking Offenders Using Automatic License-Plate Recognition

2020 ◽  
Vol 55 (4) ◽  
Author(s):  
Chuan Pratama ◽  
Suci Aulia ◽  
Dadan Nur Ramadan ◽  
Sugondo Hadiyoso
Keyword(s):  
Detection Methods ◽  
Prototype System ◽  
License Plate ◽  
License Plate Recognition ◽  
Test Results ◽  
Accuracy Rate ◽  
Road Space ◽  
Camera Position ◽  
License Plate Detection ◽  
Automatic License Plate Recognition

Vehicles parked illegally on the highway can limit road space and result in congestion. Thus, illegal parking must be monitored and controlled. In this study, a prototype system for detecting the license plates of parking offenders based on image processing was implemented. The first stage in this system is detecting the license plate, then segmenting each character into a separate image. The next stage is converting the character from image to text format, referred to as automatic license-plate recognition. The goal is to send that detected plate license to the database of the authorities, so that the authorities can discover the identity of the parking offender to impose sanctions. In this study, several conditions of acquisition and variations of edge detection methods were tested. Based on the test results, an accuracy rate of 100% was obtained for license plate detection using the Canny method during the morning, with the camera position at 3 meters high, 2 meters of distance, and a 60o angle.

IMAGE PROCESSING OF ANDROID-BASED PATROL ROBOT FEATURING AUTOMATIC LICENSE PLATE RECOGNITION

2015 ◽  
Vol 3 (3) ◽  
pp. 296-301
Author(s):  
S. Madhan ◽  
M. Pradeep
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Rate ◽  
License Plate ◽  
License Plate Recognition ◽  
Vertical Edge ◽  
Stain Removal ◽  
License Plate Detection ◽  
Novel Methods ◽  
Automatic License Plate Recognition

This work develops an Android-based robot featuring automatic license plate recognition and automatic license plate patrolling. The automatic license plate recognition feature combines 4 self-developed novel methods, Wiener deconvolution vertical edge enhancement, AdaBoost plus vertical-edge license plate detection, vertical edge projection histogram segmentation stain removal, and customized optical character recognition. Besides, the automatic license plate patrolling feature also integrates 3 novel methods, HL2-band rough license plate detection, orientated license plate approaching, and Ad-Hoc-based remote motion control. Implementation results show the license plate detection rate and recognition rate of the Android-based robot are over 99% and over 98%, respectively, under various scene conditions. Especially, the execution time of license plate recognition, including license plate detection, is only about 0.7 second per frame on the Android-based robot.

scholarly journals Recognition of Hybrid Graphic-Text License Plates

2021 ◽  
Vol 25 (4) ◽  
pp. 416-422
Author(s):  
John Anthony C. Jose ◽  
◽  
Allysa Kate M. Brillantes ◽  
Elmer P. Dadios ◽  
Edwin Sybingco ◽  
...  
Keyword(s):  
Temporal Information ◽  
License Plate ◽  
Motion Information ◽  
License Plate Recognition ◽  
Still Images ◽  
License Plate Detection ◽  
Tracking And Recognition ◽  
Automatic License Plate Recognition

Most automatic license-plate recognition (ALPR) systems use still images and ignore the temporal information in videos. Videos provide rich temporal and motion information that should be considered during training and testing. This study focuses on creating an ALPR system that uses videos. The proposed system is comprised of detection, tracking, and recognition modules. The system achieved accuracies of 81.473% and 84.237% for license-plate detection and classification, respectively.

Sign in / Sign up

Export Citation Format

Share Document