A Brazilian License Plate Recognition Method for Applications in Smart Cities

Greati, Vitor Rodrigues; Ribeiro, Vinícius Campos Tinoco; Silva, Ivanovitch; Martins, Allan de Medeiros

doi:10.1109/s3c.2017.8501395

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…In recent times, smart cities have been a popular trend, resulting in the pacing of the development of several enabling technologies. One of them is robust automatic license plate recognition (ALPR) [1], which offers a computer vision based solution for intelligent transportation systems (ITS) [2]. In this regard, a passive mesh of cameras is generally installed at road intersections and other suitable locations to observe vehicle routing through urban environments [3].…”

Section: Introductionmentioning

confidence: 99%

Multi-String Missing Characters Restoration for Automatic License Plate Recognition System

KHAN¹,

ALI²,

SIDDIQ³

et al. 2023

IJACSA

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Developing a license plate recognition system that can cope with unconstrained real-time scenarios is very challenging. Additional cues, such as the color and dimensions of the plate, and font of the text, can be useful in improving the system's accuracy. This paper presents a deep learning-based plate recognition system that can take advantage of the bilingual text in the license plates, as used in many countries, including Saudi Arabia. We train and test the model using a custom dataset generated from real-time traffic videos in Saudi Arabia. Using the English alphanumeric alone, the accuracy of our system was on par with the existing state-of-the-art algorithms. However, it increased significantly when the additional information from the detection of Arabic text was utilized. We propose a new algorithm to restore noise-affected missing or misidentified characters in the plate. We generated a new test dataset of license plates to test how the proposed system performs in challenging scenarios. The results show a clear advantage of the proposed system over several commercially available solutions, including Open ALPR, Plate Recognizer, and Sighthound.

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Section: Introductionmentioning

confidence: 99%

Multi-String Missing Characters Restoration for Automatic License Plate Recognition System

KHAN¹,

ALI²,

SIDDIQ³

et al. 2023

IJACSA

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“…The rapid growth in human population and economic development have led to the increase in the number of vehicles annually [3]. Therefore, LPR has an important role in many applications such as in parking management systems, traffic surveillance system, toll gate and highway checking, speed checking, vehicle location and navigation, security and public safety [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Efficient Thinning Algorithm for Malaysian Car Plate Character Recognition

Shitu

Abu-Bakar²,

Tahir³

et al. 2021

Elektrika

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The thinning algorithm is one of the approaches of identifying each character printed on the car plate. Malaysian car plate characters appear in different character sizes, styles, customized printed characters etc. These variations contribute to difficulty in thinning successfully segmented and extracted license plate characters for recognition. To address these problems, an improved thinning operation for Malaysian car plate character recognition is proposed. In this algorithm, samples from segmented and extracted license plates are used for a thinning operation which is passed to Zhang-Suen thinning algorithm that could not guarantee one pixel thick and then to single pixelate algorithm that provides one pixel width of character for recognition. From the simulation, the result obtained has clearly proven to be the best for character recognition systems with least number of white pixels (777 pixels) and 0.26% redundant pixel left in the medial curve.

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