An optimized license plate recognition system for complex situations

Qiu, Jianing; Zhu, Naida; Wei, Yang; Yu, Xiaoqing

doi:10.1109/icalip.2016.7846647

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…The Tesser act engine and neural network were used for optical character recognition (OCR) to convert the license plate image into machine-encoded text. Qiu et al [20] integrated color and edge detection methods to increase the success rate of locating license plates. They employed connected component analysis and vertical projection methods alternatively to improve the precision and efficiency of segmentation.…”

Section: Recent Workmentioning

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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Section: Recent Workmentioning

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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“…The license plate recognition requires extraction and identification of the moving number plate from complex background (Qiu, Zhu, Wei, & Yu, 2016). Through license plate detection, image processing, feature extraction, character recognition in computer vision, it is needed to recognize the brand, color and other information of a vehicle (Muammer, 2014).…”

Section: Introductionmentioning

confidence: 99%

Rotation correction for license plate recognition

Nguyen

Yan

2018

2018 4th International Conference on Control, Automation and Robotics (ICCAR)

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The focus of this thesis is on reliable and robust license plate recognition (LPR). The technology is currently in operation that both the quantity and quality-based approaches are needed. The entire procedure of license plate recognition consists of six steps: (1) image acquisition, (2) image pre-processing, (3) plate locating, (4) character segmentation, (5) character recognition, (6) output. However, when a road is uneven and slant, a vehicle will be shaky while running; consequently, the plate is also unstable and tilted with an angle of rotation at this moment. Meanwhile, a surveillance camera is very difficult to capture an effective image so that the plate is hard to be located and recognized. Due to this existing problem, the contributions of this thesis are: (1) plate tilt correction using Hough transform; (2) GNN-based plate number recognition. The novelties of this thesis are to improve the robustness and reliability of license plate recognition through literature review as well as evaluate those existing technologies.

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