Multinational vehicle license plate detection in complex backgrounds

Asif, Muhammad Rizwan; Qi, Chun; Hussain, Sajid; Fareed, Muhammad Sadiq; Khan, Subhan

doi:10.1016/j.jvcir.2017.03.020

Cited by 32 publications

(24 citation statements)

References 14 publications

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“…In this study, MCT (Modified Census Transform) features [8][9][10] and Adaboost learning algorithm [11][12][13][14] are used to extract candidate character regions from an image.…”

Section: Extraction Of Candidate Areasmentioning

confidence: 99%

Extraction of Character Regions through Machine Learning and Filtering

2019

IJRTE

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 Abstract: Characters in images are able to provide main information of the image. Therefore, it is important to analyze various kinds of image data and accurately extract the characters in images. This study proposes a new method of excluding background regions and accurately detecting character regions from input images with the uses of MCT features and Adaboost algorithm. The proposed method first extracts candidate character regions from input images with the uses of MCT features and Adaboost algorithm. It then excludes non-character regions and detects real character regions from the extracted candidate regions with the use of geometrical features. In the experiment of this study, the proposed method more robustly detected character regions from various input color images than a conventional method. For performance comparison, this study compared the method based on existing texture analysis and the proposed method. In this study, to qualitatively evaluate the performance of the proposed method of extracting license plate regions, the accuracy measure was defined. The measure is used to show the ratio of the accurately extracted character regions to all character regions of an image. The conventional method using the frequency factor-based texture information had many errors of character region detection, since it failed to execute binarization of background and character regions properly. On contrary, the proposed method made use of MCT features andAdaboost algorithm, effectively filtered candidate regions with the use of geometrical features, so that it detected character regions more accurately. The proposed character detection method is expected to be usefully applied to the fields of pattern recognition and image processing, such as store sign recognition and license plate recognition.

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“…In this study, MCT (Modified Census Transform) features [8][9][10] and Adaboost learning algorithm [11][12][13][14] are used to extract candidate character regions from an image.…”

Section: Extraction Of Candidate Areasmentioning

confidence: 99%

Extraction of Character Regions through Machine Learning and Filtering

2019

IJRTE

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“…In natural environment, the background of an automobile image is complex and the illumination is stable (Asif, Chun, Hussain, Fareed, & Khan, 2017). To accurately determine the region of license plate in the natural background is the key problem for identification.…”

Section: ) License Plate Locationmentioning

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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“…LPR module can be further split into license plate character segmentation (LPCS) and license plate character recognition (LPCR). In recent years, particularly focused work has been seen for multinational ALPR systems, but it has been done only for license plate detection and verification [1][2][3][4][5]. Every module has its own importance but LPR is comparatively more important as most required information is extracted at this stage and this is a difficult task, specifically in the case of multi-style LPs for multinational vehicles.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Approach for Multi-National Vehicle License Plate Recognition Using Multi-Level Deep Features and Foreground Polarity Detection Model

Asif

Khan

2020

Applied Sciences

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License plate recognition system (LPR) plays a vital role in intelligent transport systems to build up smart environments. Numerous country specific methods have been proposed successfully for an LPR system, but there is a need to find a generalized solution that is independent of license plate layout. The proposed architecture is comprised of two important LPR stages: (i) License plate character segmentation (LPCS) and (ii) License plate character recognition (LPCR). A foreground polarity detection model is proposed by using a Red-Green-Blue (RGB) channel-based color map in order to segment and recognize the LP characters effectively at both LPCS and LPCR stages respectively. Further, a multi-channel CNN framework with layer aggregation module is proposed to extract deep features, and support vector machine is used to produce target labels. Multi-channel processing with merged features from different-level convolutional layers makes output feature map more expressive. Experimental results show that the proposed method is capable of achieving high recognition rate for multinational vehicles license plates under various illumination conditions.

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Multinational vehicle license plate detection in complex backgrounds

Cited by 32 publications

References 14 publications

Extraction of Character Regions through Machine Learning and Filtering

Extraction of Character Regions through Machine Learning and Filtering

Rotation correction for license plate recognition

An Adaptive Approach for Multi-National Vehicle License Plate Recognition Using Multi-Level Deep Features and Foreground Polarity Detection Model

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