A real time vehicle's license plate recognition system

Rahman, C.A.; Badawy, W.; Radmanesh, A.

doi:10.1109/avss.2003.1217917

Cited by 101 publications

(43 citation statements)

References 4 publications

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“…Herein, an enhanced approach for character segmentation is proposed. Some of authors such as [28,29] have previously utilized histogram based segmentation which does not almost pay attention to the very close or overlapped characters in some plates. Such overlaps could be from a stain, smudge or generally noises.…”

Section: Segmentationmentioning

confidence: 99%

A Novel Approach for Vehicle License Plate Localization and Recognition

Dashtban¹,

Dashtban²,

Bevrani³

2011

IJCA

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In this paper, a general approach for international vehicle license plate localization and recognition is proposed. A hybrid solution is presented with combining basic machine vision techniques and neural networks. The proposed model consists of three main parts, including localization, segmentation and recognition. In the license plate localization, after some essential preprocessing and finding edges, the 8-connectivity of image background eliminates which helps more appropriately separating of main image objects from the cluttered backgrounds. Then, it is tried to find connected objects with 8-connectivity of the differentiated binary image. The binarization of license plate is based on local binarizing. The proposed recognizing system utilizes the Hough transform, basic morphological operators and Skeletonizing to provide an appropriate input for artificial neural networks. Segment by segment, the input streams into an intelligent error control unit (IECU) which itself is an already trained multi-layer perceptron (MLP) neural network. IECU investigates empty or non-character-inside segments. In case of no error, each segment streams into two already trained MLPs. Each of them singly recognizes either the alphabets or numbers. We show that this approach achieves accuracy over 91% on localizing vehicle license plate. The image database includes images of various vehicles with different background and slop under varying illumination conditions. The character recognition system correctly recognizes alphabets with probability over 97% and over 94% in case of numbers.

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Section: Segmentationmentioning

confidence: 99%

A Novel Approach for Vehicle License Plate Localization and Recognition

Dashtban¹,

Dashtban²,

Bevrani³

2011

IJCA

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“…In [1]- [4], [15] are real-time license plate recognition, and in [5]- [8], [16] are make a lot of ways the license plate location. In [1] use HSI color space method.…”

Section: Research Of Related Literaturementioning

confidence: 99%

“…In [8] need to find the thresholds of row and column, but affected by light or night and other factors, it cannot find the ideal thresholds. In [15] window to scan, scan window area through two times Haar Discrete Wavelet Transform, each image after the conversion into HH, HL, LH, LL and other four part, where LL is a low frequency images and extract the LL low-frequency parts of the image, and using artificial neural network to a limited area of vehicle license plate recognition. Therefore, this method of vehicle license plate character segmentation problems can be avoided.…”

Section: Research Of Related Literaturementioning

confidence: 99%

Haar Discrete Wavelet-Based and Scanning Window for Vehicle License Plate Recognition

Lee¹,

Hung²

2017

JSW

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This paper is the study of vehicle license plate recognition. We use the scan window, 2-D Haar Discrete Wavelet Transform and Artificial Neural Networks. This paper consists of three main parts. First part is to locate and extract license plate. Second part is the Artificial Neural Network training of the license plate. Third part is a real time scans recognition. We use two times Haar Discrete Wavelet Transform image, and extract low frequency part LL of vehicles license plate image, makes image coefficient became original of one-sixteen, thus reducing image coefficients, so can to fast recognition.This method is to use scan the license plate recognition, but we do not license plate recognition for each character. Experiment result can be a high recognition rate.

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“…Others turn to the field of projection profiles and character contours [13][14]. The best effect of license plate segmentation is the combined features [15,16].…”

Section: Introductionmentioning

confidence: 99%