Study of Deep Learning Algorithms for Automatic License Plate Recognition (ALPR)

Mahalakshmi, S.

doi:10.22214/ijraset.2018.5249

Cited by 5 publications

(10 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another essential technique is edge detection. Edge detection aims at finding the changes in brightness in an image to be used for capturing the critical event [11,12]. The two of them are usually combined [13].…”

Section: Related Workmentioning

confidence: 99%

Indonesian license plate recognition with improved horizontal-vertical edge projection

Nurhaida

Nududdin

Ramayanti

2021

IJEECS

View full text Add to dashboard Cite

<p>License plate recognition (LPR) is one of the classical problems in the field of object recognition. Its application is very crucial in the automation of transportation system since it helps to recognise a vehicle identity, which information is stored in the license plate. LPR usually consists of three major phases: pre-processing, license plate localisation, optical character recognition (OCR). Despite being classical, its implementation faced with much more complicated problems in the real scenario. This paper proposed an improved LPR algorithm based on modified horizontal-vertical edge Projection. The method uses for detecting and localising the region of interest. It is done using the horizontal and vertical projection of the image. Related works proved that the modified horizontal-vertical edge projection is the simplest method to be implemented, yet very effective against Indonesian license plate. However, its performance gets reduced when specular reflection occurs in the sample image. Therefore, morphological operations are utilised in the pre-processing phase to reduce such effects while preserving the needed information. Eighty sample images which captured using various camera configurations were used in this research. Based on the experimental results, our proposed algorithm shows an improvement compared with the previous study and successfully detect 71 license plates in 80 image samples which results in 88.75% accuracy.</p>

show abstract

Section: Related Workmentioning

confidence: 99%

Indonesian license plate recognition with improved horizontal-vertical edge projection

Nurhaida

Nududdin

Ramayanti

2021

IJEECS

View full text Add to dashboard Cite

show abstract

“…The number of vehicles has been increasing in the last few years, and the ALPR has become a very important tool that helps in the monitoring and control of the vehicles. However, the existing diversity of plate formats, different scales, rotations, and no uniform illumination conditions during capturing the image [5] have become a big issue for the ALPR system. There are several methods and researches about ALPR.…”

Section: Related Workmentioning

confidence: 99%

“…ALPR has become a very important tool in the ITS field because this service helps in the monitoring and control of the vehicles. However, due to the diversity of plate formats (for example plate size, plate background, character size, plate texture and so on), the accurate development of an ALPR system is a challenging task, mainly when it comes to an open environment, where there are variations in the illumination conditions during image capturing [5].…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Automatic Plate Recognition System Based on Optical Character Recognition and Wireless Sensor Networks for ITS

Dalarmelina

Teixeira

Meneguette

2019

Sensors

View full text Add to dashboard Cite

Automatic License Plate Recognition has been a recurrent research topic due to the increasing number of cameras available in cities, where most of them, if not all, are connected to the Internet. The video traffic generated by the cameras can be analyzed to provide useful insights for the transportation segment. This paper presents the development of an intelligent vehicle identification system based on optical character recognition (OCR) method to be used on intelligent transportation systems. The proposed system makes use of an intelligent parking system named Smart Parking Service (SPANS), which is used to manage public or private spaces. Using computer vision techniques, the SPANS system is used to detect if the parking slots are available or not. The proposed system makes use of SPANS framework to capture images of the parking spaces and identifies the license plate number of the vehicles that are moving around the parking as well as parked in the parking slots. The recognition of the license plate is made in real-time, and the performance of the proposed system is evaluated in real-time.

show abstract

“…[2]. For better recognition of number plates, intelligent algorithms are required [10]. Several techniques were proposed to improve the system by many research groups.…”

mentioning

confidence: 99%

“…Several techniques were proposed to improve the system by many research groups. Therefore, this research is a challenging task and is restricted locally [1,2]; Character recognition methods should deal with all these defects [1,10,19]. Ambiguous characters on number plates are critical challenges in the industry and are limiting the growth of the ANPR system market [3,28].…”

mentioning

confidence: 99%

A Character Segmentation Method to Increase Character Recognition Accuracy for Turkish Number Plates

Gç¹,

Gokmen²

2021

Preprint

View full text Add to dashboard Cite

Automatic Number Plate Recognition is a computer vision technology that provides a way to recognize the vehicles number plates without direct human intervention. Developing Automatic Number Plate Recognition methodologies is a widely studied topic among the computer vision community to increase the accuracy rates. Although there are many studies, the research in character segmentation and improving the recognition accuracy remains limited. In this study, a new methodology is proposed to reduce the character recognition errors of Automatic Number Plate Recognition systems. To achieve this, it will be determined whether the characters are letters or numbers, and the number plates will be expressed in the form of letters - digit. The method suggested for segmenting blobs correctly worked with an accuracy of 96.12% on the test dataset. The method suggested for generating letter-digit expression for the number plates correctly worked with an accuracy of 99.28% on the test dataset. The proposed methodology can work only on Turkish number plates. In the future studies, the proposed methodology can be expanded by using the number plate dataset of a different country.

show abstract

Study of Deep Learning Algorithms for Automatic License Plate Recognition (ALPR)

Cited by 5 publications

References 2 publications

Indonesian license plate recognition with improved horizontal-vertical edge projection

Indonesian license plate recognition with improved horizontal-vertical edge projection

A Real-Time Automatic Plate Recognition System Based on Optical Character Recognition and Wireless Sensor Networks for ITS

A Character Segmentation Method to Increase Character Recognition Accuracy for Turkish Number Plates

Contact Info

Product

Resources

About