Vehicle license-plate recognition by image analysis

Miyamoto, Kazuhito; Nagano, Koki; Tamagawa, Mitsuaki; Fujita, Ichiro; Yamamoto, Masaaki

doi:10.1109/iecon.1991.239253

Cited by 40 publications

(19 citation statements)

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“…Edge-based methods [7][8][9][10][11][12] are one of the simplest approaches. They scan images to find areas on which vertical and horizontal edges overlap and, at the same time, of which shapes are rectangles with the ratio of width to height close to those of plates.…”

Section: Related Workmentioning

confidence: 99%

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Vehicle Plate Detection in Car Black Box Video

Park¹,

Jun

2017

Advances in Multimedia

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Internet services that share vehicle black box videos need a way to obfuscate license plates in uploaded videos because of privacy issues. Thus, plate detection is one of the critical functions that such services rely on. Even though various types of detection methods are available, they are not suitable for black box videos because no assumption about size, number of plates, and lighting conditions can be made. We propose a method to detect Korean vehicle plates from black box videos. It works in two stages: the first stage aims to locate a set of candidate plate regions and the second stage identifies only actual plates from candidates by using a support vector machine classifier. The first stage consists of five sequential substeps. At first, it produces candidate regions by combining single character areas and then eliminates candidate regions that fail to meet plate conditions through the remaining substeps. For the second stage, we propose a feature vector that captures the characteristics of plates in texture and color. For performance evaluation, we compiled our dataset which contains 2,627 positive and negative images. The evaluation results show that the proposed method improves accuracy and sensitivity by at least 5% and is 30 times faster compared with an existing method.

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Section: Related Workmentioning

confidence: 99%

“…When searching such areas, Hough transform is adopted in [9,10] to detect even rotated plates. To determine boundaries of rectangles, connected component analysis (CCA) [11] or template matching [12] is used. In general, the edge-based approaches are simple to implement but require that all edge pixels are connected.…”

Section: Related Workmentioning

confidence: 99%

Vehicle Plate Detection in Car Black Box Video

Park¹,

Jun

2017

Advances in Multimedia

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“…Most of the algorithms have limiting conditions (Emiris and Koulouriotis, 2001), such as fixed illumination (Davies et al, 1990), fixed type of license plate, stationary backgrounds (Salgado et al, 1999), prescribed driveways (Parisiet al, 1998) (Miyamoto et al, 1991), limited vehicle speeds (Adorni et al, 1998;Broumandnia and Fathi, 2005).…”

Section: Jcsmentioning

confidence: 99%

Egyptian License Plate Recognition Using Enhanced Stroke Width Transformation and Fuzzy Artmap

Youssef¹,

El-Mahallawy²,

Badr³

2014

Journal of Computer Science

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License Plate Recognition (LPR) is the most important type of Intelligent Transportation System (ITS). LPR is used in many different types of ITS like electronic payment systems, toll station, parking fees, freeway and arterial management systems for traffic surveillance. Few years ago, Egyptians government changed the car license plate to include letters and numbers. So the needs for efficient LPR System for the new license plate are increased in different ITS fields. This study presents an enhanced LPR detection algorithm for the new Egyptian licenses plate. The detection enhancement is done using Stroke Width Transform algorithm to extract letters from candidate areas combined with Fuzzy ARTMAP classifier. Stroke Width Transform (SWT) is a state of art algorithm developed by Microsoft Research Lab for detecting text in natural scene, it seeks to find the value of stroke width for each image pixel and demonstrate its use on the task of text detection in natural images. This study is focusing on detecting Arabic letters in the candidate license plate area using SWT image map instead of binary image map where not all Arabic letters have uniformly stroke width and some letters have a dot above and below it. The proposed model shows 26% detection accuracy enhancement than conventional LPR systems (Sobel Edge detection with binary image map using template matching technique).

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“…Due to different working environments, LPR techniques vary from application to application. Most previous works have in some way restricted their working conditions [9], such as limiting them to indoor scenes, stationary backgrounds [30], fixed illumination [7], prescribed driveways [22], [26], limited vehicle speeds [1], or designated ranges of the distance between camera and vehicle [23]. The aim of this study is to lessen many of these restrictions.…”

Section: Introductionmentioning

confidence: 99%