Bangla license plate reader for metropolitan cities of Bangladesh using template matching

Baten, Raiyan Abdul; Omair, Zunaid; Sikder, Urmita

doi:10.1109/icece.2014.7026925

Cited by 21 publications

(7 citation statements)

References 8 publications

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“…Table 3 shows comparative accuracies and computational processing time between the proposed system and the systems employed in relevant literature. In [30], the authors estimated that 1.3 s was taken to complete the image processing, whereas the proposed system in this experiment took, on average, only 111 milliseconds to complete the whole process. A Graphics Processing Unit (GEFORCE RTX 2070 super) with 16 GB RAM was used in this system to perform quick computation.…”

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confidence: 87%

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Intelligent System for Vehicles Number Plate Detection and Recognition Using Convolutional Neural Networks

et al. 2021

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Vehicles on the road are rising in extensive numbers, particularly in proportion to the industrial revolution and growing economy. The significant use of vehicles has increased the probability of traffic rules violation, causing unexpected accidents, and triggering traffic crimes. In order to overcome these problems, an intelligent traffic monitoring system is required. The intelligent system can play a vital role in traffic control through the number plate detection of the vehicles. In this research work, a system is developed for detecting and recognizing of vehicle number plates using a convolutional neural network (CNN), a deep learning technique. This system comprises of two parts: number plate detection and number plate recognition. In the detection part, a vehicle’s image is captured through a digital camera. Then the system segments the number plate region from the image frame. After extracting the number plate region, a super resolution method is applied to convert the low-resolution image into a high-resolution image. The super resolution technique is used with the convolutional layer of CNN to reconstruct the pixel quality of the input image. Each character of the number plate is segmented using a bounding box method. In the recognition part, features are extracted and classified using the CNN technique. The novelty of this research is the development of an intelligent system employing CNN to recognize number plates, which have less resolution, and are written in the Bengali language.

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confidence: 87%

“…Training: 500 Testing: 200 100% 98.2% 111 milliseconds for the whole process [29] Testing: 300 84% 80% - [30] Testing: 120 --1.3 s [31] Testing: 119 95.8% 84.87% - [25] Training: 450 Testing: 50 88.67% --…”

Section: Proposed Systemmentioning

confidence: 99%

Intelligent System for Vehicles Number Plate Detection and Recognition Using Convolutional Neural Networks

et al. 2021

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“…Because it may compromise the identification accuracy of these systems, the character recognition module is also regarded as a vital part of segmentation-based CAPTCHA recognition systems. By comparing distinct characters with template characters using character coefficient values, the template matching [13,14,15] technique is used to identify characters. If more sophisticated techniques are not used, errors resulting from character similarity are seen as a weak spot in recognition by template matching.…”

Section: IImentioning

confidence: 99%

Four-Character Captcha Recognition using Deep Learning Techniques

Sahith¹,

Swethan²,

Naresh³

2022

IJRASET

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bstract: The Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) is a crucial humanmachine distinguishing technique for websites to fend against automated attacks from malicious software. Studies on CAPTCHA recognition have the potential to uncover security flaws, improve CAPTCHA technology, and even advance handwriting and license plate recognition. However, the whole objective of the CAPTCHAs may be bypassed using the principles of deep learning and computer vision. Convolutional neural networks (CNN) may be used to automatically pass this exam. A CNN is a deep learning algorithm that uses an image as input before giving distinct characteristics in the picture a value that further aids in differentiating one feature from another. Its major objective is to convert the pictures into a more simple form while retaining elements that are crucial for obtaining an optimum forecast.

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“…The character recognition module is also considered a crucial component of segmentation-based CAPTCHA recognition systems because it can influence the recognition accuracy of these systems. Sakkatos et al [27] used the template matching [28,29] approach to recognize characters by comparing the separate characters with template characters using character's coefficient values. Errors arising from similarities between characters are considered a weak point in recognition via template matching unless more advanced solutions are incorporated.…”

Section: Related Workmentioning

confidence: 99%

CAPTCHA Recognition Using Deep Learning with Attached Binary Images

et al. 2020

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Websites can increase their security and prevent harmful Internet attacks by providing CAPTCHA verification for determining whether end-user is a human or a robot. Text-based CAPTCHA is the most common and designed to be easily recognized by humans and difficult to identify by machines or robots. However, with the dramatic advancements in deep learning, it becomes much easier to build convolutional neural network (CNN) models that can efficiently recognize text-based CAPTCHAs. In this study, we introduce an efficient CNN model that uses attached binary images to recognize CAPTCHAs. By making a specific number of copies of the input CAPTCHA image equal to the number of characters in that input CAPTCHA image and attaching distinct binary images to each copy, we build a new CNN model that can recognize CAPTCHAs effectively. The model has a simple structure and small storage size and does not require the segmentation of CAPTCHAs into individual characters. After training and testing the proposed CAPTCHA recognition CNN model, the achieved experimental results reveal the strength of the model in CAPTCHA character recognition.

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Bangla license plate reader for metropolitan cities of Bangladesh using template matching

Cited by 21 publications

References 8 publications

Intelligent System for Vehicles Number Plate Detection and Recognition Using Convolutional Neural Networks

Intelligent System for Vehicles Number Plate Detection and Recognition Using Convolutional Neural Networks

Four-Character Captcha Recognition using Deep Learning Techniques

CAPTCHA Recognition Using Deep Learning with Attached Binary Images

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