Bangladeshi License Plate Detection and Recognition with Morphological Operation and Convolution Neural Network

Rabbani, Golam; Islam, Mohammad Aminul; Azim, Muhammad Anwarul; Islam, Mohammad Khairul; Rahman, Md. Mostafizur

doi:10.1109/iccitechn.2018.8631937

Cited by 22 publications

(10 citation statements)

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“…Convolutional Neural Networks is used in [ 78 ] in real time scenario and has shown great results for each stage of ANPR system. Neural network based algorithms seems promising for ANPR and are proposed in [ 19 , 103 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 ].…”

Section: Discussionmentioning

confidence: 99%

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Lubna

Mufti²,

Shah

2021

Sensors

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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.

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Section: Discussionmentioning

confidence: 99%

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Lubna

Mufti²,

Shah

2021

Sensors

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“…In [2], authors proposed a way to extract plate; they used Sliding Concentric Window for division the interested region and morphological( dilation and erosion); the accuracy of the system was 86.5 %. In [3], the haze removal technique was employed to enhance the images. Then the improved image 1089 converted to grayscale, with the Wiener filter for removing the noise and then binaries the de-noising image were used to achieve connected component labeling.…”

Section: Introductionmentioning

confidence: 99%

Vehicle plate localization and extraction based on hough transform and bilinear operations

Uthaib

Croock²

2020

IJEECS

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<p>In general, the extraction of the vehicle plate is a previous step of plate recognition, and it actively studied for several decades. Plate localization is used in various security and traffic applications. In this paper, the proposed method is efficient to localize a plate for the multinational countries. The proposed method consists of three levels. The first level is the preprocessing<strong> </strong>that contains several steps. The digital camera capture images have been taken about twenty meters from the car with zooming two to three meters. Images are resampled using the zooming technique (bilinear interpolation) that makes the dimension of image (1024 x 768) pixels. The resampled images are resized to (940x 730). These images are converted to grayscale (green channel), and the bilateral filter is applied for removing noise. The second level is plate detection that adopts morphological operations, image subtraction, and vertical edge detection (Sobel). At the last Connected component analysis and Hough transform are used. The third level is the Deskew plate that notifies the plate may skew so that Hough transform is used to detect the largest line segment. Then, the images are rotated using bilinear interpolation. About 860 images are tested for different countries (Iraq, Belarus, Armenia, Hungary), and the accuracy is 98.99 % for extraction of the plate and 100% for the Deskew plate. Thus, the proposed system shows high efficiency in achievement.</p>

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“…This system used small dataset, and need high resolution image to detect broken characters for the characters segmentation. [7] Yuan et al proposed the robust and efficient techniques for the real time detection of license plate in complex scenes by applying novel line density filter (LDF) for candidate extraction and cascaded license plate classifier (CLPC) for candidate verification. Downsampling and gray scale conversion of image are used in the pre-processing step.…”

Section: Introductionmentioning

confidence: 99%

Study for License Plate Detection

Aung¹,

Khaing²,

San³

2019

IJIGSP

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License Plate Detection (LPD) system is the application of computer vision and image processing technology. LPD system is the first and main step of License Plate Recognition (LPR) system. So, it performs as the main driver of the LPR system. License plate detection step is always performed in front of the license plate recognition step. LPD system takes the vehicle images as input, follows with the general steps: such as reprocessing, localization, region extraction, and region detection, and the detected image are the output of the system. There are many algorithms for LPD while detecting a license plate in different conditions is still a complex task. For the LPD system, morphological operation and deep learning model are mostly used. This paper presents the critical study of the license plate detection system and also examines the implementation of new technologies of the license plate detection system.

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Bangladeshi License Plate Detection and Recognition with Morphological Operation and Convolution Neural Network

Cited by 22 publications

References 5 publications

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Vehicle plate localization and extraction based on hough transform and bilinear operations

Study for License Plate Detection

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