A style-free and high speed algorithm for License Plate Detection

Shahbahrami, Asadollah; Fomani, Babak Abad; Akoushideh, Alireza

doi:10.1109/iranianmvip.2017.8342372

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…For example, the method failed to accurately identify some characters, that is, the presence of thin letters, which are difficult to handle and lead to a significant segmentation error. As a result, in order to prevent these defects, an algorithm is used to maximize the image's contrast, as has been used in several studies [ 25 , 26 ].…”

Section: Related Workmentioning

confidence: 99%

Deep‐Learning‐Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition

Habeeb

Noman

Alkahtani

et al. 2021

Computational Intelligence and Neuroscience

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Recognizing vehicle plate numbers is a key step towards implementing the legislation on traffic and reducing the number of daily traffic accidents. Although machine learning has advanced considerably, the recognition of license plates remains an obstacle, particularly in countries whose plate numbers are written in different languages or blended with Latin alphabets. This paper introduces a recognition system for Arabic and Latin alphabet license plates using a deep-learning-based approach in conjugation with data collected from two specific countries: Iraq and Malaysia. The system under study is proposed to detect, segment, and recognize vehicle plate numbers. Moreover, Iraqi and Malaysian plates were used to compare these processes. A total of 404 Iraqi images and 681 Malaysian images were tested and used for the proposed techniques. The evaluation took place under various atmospheric environments, including fog, different contrasts, dirt, different colours, and distortion problems. The proposed approach showed an average recognition rate of 85.56% and 88.86% on Iraqi and Malaysian datasets, respectively. Thus, this evidences that the deep-learning-based method outperforms other state-of-the-art methods as it can successfully detect plate numbers regardless of the deterioration level of image quality.

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Section: Related Workmentioning

confidence: 99%

Deep‐Learning‐Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition

Habeeb

Noman

Alkahtani

et al. 2021

Computational Intelligence and Neuroscience

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“…Many different procedures for number plate detection and extraction have been recently proposed, such as the fuzzified Gabor filter [1], the method based on the wavelet transform [11], attribute filtering [12], edge detection and noise reduction [13], and mathematical morphology [14]. Each of these approaches has its own limitations and advantages [15], [16], [17]. The quality of the cropped license plates resulting from these procedures is primarily influenced by the quality of the camera that captures the source image and by the quality of the algorithm used for the image processing.…”

Section: Recent Workmentioning

confidence: 99%

“…Two output segments and the triangular membership function were used to describe the output and achieve the smallest deviations from the central wavelength values. For the central input values of 9 and 15 pixels, the selected intervals were [8,10] and [14,16]. These functions have fully described the input and the output of the fuzzy system.…”

Section: Fuzzified Gabor Filtermentioning

confidence: 99%

Comparison of Gabor Filter Bank and Fuzzified Gabor Filter for License Plate Detection

Tadić¹,

Király²,

Ódry³

et al. 2020

ACTA POLYTECH HUNG

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This paper presents a comparative study of improvements to the algorithms for license plate extraction from images captured using conventional, modest-quality cameras. It compares the results and efficacy of two similar algorithms which primarily differ in the raw image pre-processing stage of the initial image. One algorithm uses the Gabor filter bank with distinctly crisp parameters; the other relies on the fuzzified Gabor filter bank with fuzzified parameters to facilitate their adjustment. Results indicate that the fuzzy reasoning introduced for Gabor filter parameter adjustment improves the detection of the components of interest in complex images and adds minimal deviation compared to the Gabor filter bank with distinctly crisp parameters.

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“…Since the license plate localization is the first and essential step of the recognition process, the result has a direct impact on the accuracy of character segmentation and character recognition. However, the license plate can be easily affected by external factors such as lightning conditions, weather, and backgrounds; besides, most VPLR systems do not fully consider the complexity of background and illumination conditions in the practical application, so locating and detecting the license plate from original images accurately and efficiently are still vital steps 2 Journal of Advanced Transportation and the main difficulties for successful license plate recognition [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A Robust License Plate Detection and Character Recognition Algorithm Based on a Combined Feature Extraction Model and BPNN

Xie

Ming

Zhao

et al. 2018

Journal of Advanced Transportation

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The rapid development of the license plate recognition technology has made great progress for its widespread uses in intelligent transportation system (ITS). This paper has proposed a novel license plate detection and character recognition algorithm based on a combined feature extraction model and BPNN (Backpropagation Neural Network) which is adaptable in weak illumination and complicated backgrounds. Firstly, a preprocessing is first used to strengthen the contrast ratio of original car image. Secondly, the candidate regions of license plate are checked to verify the true plate, and the license plate image is located accurately by the integral projection method. Finally, a new feature extraction model is designed using three sets of features combination, training the feature vectors through BPNN to complete accurate recognition of the license plate characters. The experimental results with different license plate demonstrate effectiveness and efficiency of the proposed algorithm under various complex backgrounds. Compared with three traditional methods, the recognition accuracy of proposed algorithm has increased to 97.7% and the consuming time has decreased to 46.1ms.

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A style-free and high speed algorithm for License Plate Detection

Cited by 4 publications

References 15 publications

Deep‐Learning‐Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition

Deep‐Learning‐Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition

Comparison of Gabor Filter Bank and Fuzzified Gabor Filter for License Plate Detection

A Robust License Plate Detection and Character Recognition Algorithm Based on a Combined Feature Extraction Model and BPNN

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