License Plate Localization Algorithm Research Articles

LOKALISASI PLAT NOMOR DENGAN JAVA MENGGUNAKAN DETEKSI TEPI CANNY DAN FILTER MORFOLOGI

This study aims to introduce an automatic vehicle identification system based on vehicle licenses and license plate recognition. This Automatic Number Plate Recognition (ANPR) system software uses a series of image processing algorithms captured by a camera with a high degree of accuracy and has the efficiency to recognize license plates and then identify vehicles from datasets stored on a Personal Computer (PC). This study uses an algorithm for license plate localization by implementing the Canny edge detection algorithm and a morphological filter.The methodology used is to develop a Number Plate (NP) localization method including the following steps: (1) Color conversion, image enhancement (using sharpness filters and gamma correction), Canny edge detection and alignment (skeletonization); stage (2) morphological filter-based method to determine the Region of Interest (ROI), namely a rectangular box containing a Number Plate (NP). A rectangular bounding box is drawn across the area in the original image that contains the NP after the NP are localized. The bounding box is equal to the width of the image plus one pixel and the height of the NP plus one pixel.The results showed that this method succeeded in achieving a localization rate of about 72.0%. The method used for license plate localization is able to localize license plates with clear boundaries and unambiguous separation of other related components. This research can be further developed to include more functions so that it can be used by all elements of society from researchers to law enforcers.

Automatic Localization of License Plate for Car in Wolfram Mathematica

Modern imaging devices make it possible to solve a complex of technical applied problems that require the synthesis and analysis of computer processing methods using threshold binarization, image classification, clustering, and the use of machine learning to determine areas of interest. Thus, segmentation algorithms are widely used for processing medical images. Computer technologies are used for the functioning of the intellectual environment, which allows to analyze the state of human health. The development of microelectronics makes it possible to increase the complexity of the applied image processing algorithms used to solve applied engineering problems. The issues of segmentation, pattern recognition, description and presentation of details, morphological analysis of images obtained by industrial equipment are widely discussed in the literature. For example, theories of optical signal processing taking into account interference, issues of image perception and analysis are presented in detail in domestic and foreign literature. The paper describes the developed algorithm for localizing a car license plate, implemented in the Wolfram Mathematica system. First, the region of interest is determined, isolated from the rest of the image for its subsequent processing. An image representation is implemented using an affine transformation. Further segmentation of the characters on the license plate allows the characters to be identified. In the Mathematica system, a program code for the car license plate localization algorithm for its further recognition has been developed. The solution to the problem was obtained using the step-by-step application of the built-in and user-defined functions of the Wolfram Mathematica system. The algorithm has been tested on a representative sample of images. The average error did not exceed 10 %, which is in line with modern industrial image processing algorithms. The resulting car license plate identification algorithm can be used in digital devices to automatically determine and further image processing.

An adaptive integrated rule-based algorithm for license plate localization

AbstractThis paper addresses a license plate localization (LPL) algorithm for a complex background. Most of LPL algorithm works on restricted conditions, as well as on a principle of sequential elimination of blocks from image level to final LP candidate region. In most of algorithms, blocks are filtered out for not satisfying required LP features in a top-down approach and this may result in a poor efficiency in a complex scenario. The major steps of the proposed approach are adaptive edge mapping, saliency measure of edge based rules with confidence level estimation using fuzzy rules and final step for reassessment of decision by colour attributes filtering. The proposed algorithm is adaptive to across the country variations in LP standards, as well as it is tested on two data sets each one consisting of more than 700 images, set-1 being for good images while set-2 including only constrained images. The algorithm is tested for a low contrast due to overexposure or poor lighting, existence of multiple plates, variation in aspect ratio and compatible background conditions. It has been observed, that the performance degradation imposing complex condition is nominal.

A sliding window technique for efficient license plate localization based on discrete wavelet transform

Real-time license plate recognition (LPR) is an interesting but complicated research topic. Some previous works use discrete wavelet transform (DWT) to extract license plate (LP), however, most of them are not capable of dealing with complex environments such as the low-contrast source images and the dynamic-range problems. In this paper, we propose an algorithm for license plate localization (LPL) based on DWT. The LP can be extracted from different quality of source images under complex environments by using two frequency subbands. We first use the HL subband to search the features of LP and then verify the features by checking whether in the LH subband there exists a horizontal line around the feature or not. The proposed method can extract both front and back LPs of various vehicles. The experimental results show that the proposed method can achieve good LPL results with both short run-time and high accurate detection rate.