A review on License Plate Recognition system algorithms

Bakhtan, Muayad Ali Hamood; Abdullah, Munaisyah; Rahman, Aedah Abd

doi:10.1109/icictm.2016.7890782

Cited by 10 publications

(6 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors in [ 18 ] discussed the performance of the various algorithms used in the past. The percentage efficiency of the ANPR system per year was presented for years 1999–2015.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Lubna

Mufti²,

Shah

2021

Sensors

View full text Add to dashboard Cite

Technologies and services towards smart-vehicles and Intelligent-Transportation-Systems (ITS), continues to revolutionize many aspects of human life. This paper presents a detailed survey of current techniques and advancements in Automatic-Number-Plate-Recognition (ANPR) systems, with a comprehensive performance comparison of various real-time tested and simulated algorithms, including those involving computer vision (CV). ANPR technology has the ability to detect and recognize vehicles by their number-plates using recognition techniques. Even with the best algorithms, a successful ANPR system deployment may require additional hardware to maximize its accuracy. The number plate condition, non-standardized formats, complex scenes, camera quality, camera mount position, tolerance to distortion, motion-blur, contrast problems, reflections, processing and memory limitations, environmental conditions, indoor/outdoor or day/night shots, software-tools or other hardware-based constraint may undermine its performance. This inconsistency, challenging environments and other complexities make ANPR an interesting field for researchers. The Internet-of-Things is beginning to shape future of many industries and is paving new ways for ITS. ANPR can be well utilized by integrating with RFID-systems, GPS, Android platforms and other similar technologies. Deep-Learning techniques are widely utilized in CV field for better detection rates. This research aims to advance the state-of-knowledge in ITS (ANPR) built on CV algorithms; by citing relevant prior work, analyzing and presenting a survey of extraction, segmentation and recognition techniques whilst providing guidelines on future trends in this area.

show abstract

“…The authors in [ 18 ] discussed the performance of the various algorithms used in the past. The percentage efficiency of the ANPR system per year was presented for years 1999–2015.…”

Section: Discussionmentioning

confidence: 99%

“…There are different terminologies used for ANPR systems: Table 1 summarizes previous reviews and surveys related to ANPR [16][17][18][19]. Table 2 presents performance summary of ANPR system techniques considering their relevant performance parameters.…”

Section: Introductionmentioning

confidence: 99%

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Lubna

Mufti²,

Shah

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…ALPR is the automated process of recognizing an LP and matching the correct letters and numerical characters. Different research areas have proposed a variety of open source and commercial solutions for ALPR [ 31 ]. However, such algorithms are often tested under idealized conditions (e.g.,no reflections due to sunlight, an acute angle for capturing the LP).…”

Section: Description Of Experimental Campaign and Data Sourcesmentioning

confidence: 99%

An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation

Genser

Hautle

Makridis

et al. 2021

Sensors

View full text Add to dashboard Cite

A reliable estimation of the traffic state in a network is essential, as it is the input of any traffic management strategy. The idea of using the same type of sensors along large networks is not feasible; as a result, data fusion from different sources for the same location should be performed. However, the problem of estimating the traffic state alongside combining input data from multiple sensors is complex for several reasons, such as variable specifications per sensor type, different noise levels, and heterogeneous data inputs. To assess sensor accuracy and propose a fusion methodology, we organized a video measurement campaign in an urban test area in Zurich, Switzerland. The work focuses on capturing traffic conditions regarding traffic flows and travel times. The video measurements are processed (a) manually for ground truth and (b) with an algorithm for license plate recognition. Additional processing of data from established thermal imaging cameras and the Google Distance Matrix allows for evaluating the various sensors’ accuracy and robustness. Finally, we propose an estimation baseline MLR (multiple linear regression) model (5% of ground truth) that is compared to a final MLR model that fuses the 5% sample with conventional loop detector and traffic signal data. The comparison results with the ground truth demonstrate the efficiency and robustness of the proposed assessment and estimation methodology.

show abstract

“…ALPR is the automated process of recognizing an LP and matching the correct letters and numerical characters. Different research areas have proposed a variety of open source and commercial solutions for ALPR (19). However, such algorithms are often tested under idealized conditions (e.g.,no reflections due to sunlight).We apply a novel algorithm from (20) to our video data set to determine traffic flow and travel times.…”

Section: Data Sources For Sensor Assessmentmentioning

confidence: 99%

An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation

Genser,

Hautle,

Makridis

et al. 2021

Preprint

View full text Add to dashboard Cite

Accurate estimation of the traffic state over a network is essential since it is the starting point for designing and implementing any traffic management strategy. Hence, traffic operators and users of a transportation network can make reliable decisions such as influence/change route or mode choice. However, the problem of traffic state estimation from various sensors within an urban environment is very complex for several different reasons, such as availability of sensors, different noise levels, different output quantities, sensor accuracy, heterogeneous data fusion, and many more. To provide a better understanding of this problem, we organized an experimental campaign with video measurement in an area within the urban network of Zurich, in Switzerland. We focus on capturing the traffic state in terms of traffic flow and travel times by ensuring measurements from established thermal cameras by the city's authorities, processed video data and the Google Distance Matrix. We assess the different data sources, and we propose a simple yet efficient Multiple Linear Regression (MLR) model to estimate travel times with fusion of various data sources. Comparative results with ground-truth data (derived from video measurements) show the efficiency and robustness of the proposed methodology.

show abstract

A review on License Plate Recognition system algorithms

Cited by 10 publications

References 20 publications

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation

An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation

Contact Info

Product

Resources

About