In AVNPR system, license plate detection from original image is the most critical part. In this thesis, a new technique was developed for a license plate detection system using the Finite Ridgelet Transform (FRIT) algorithm. The proposed technique is simple and effective, which outperforms other existing methods for license plate detection (LPD).
The results of the experiments show that the proposed technique is able to successfully detect the license plate in different lighting conditions. The AVNPR system consists of three main parts: License Plate Detection (LPD), Character Segmentation (CS) and Character Recognition (CR).
Review of Previous Works and Observation
In this system, first an image is captured by a recording device and then the process of registration of number plate is carried out. Computer vision and character recognition algorithms for License Plate Recognition (LPR) are used as core modules for Automatic Vehicle Number plate Recognition (AVNPR) system [2]. The authors used the statistical information about license plate regions based on the license plate edge analysis.
All the time, the license plate may not be available in the selected areas, so it takes a long time to find the edges in that area, but the result will be zero; Therefore, these proposed methods are not suitable for real-time AVNPR systems. In this literature [15], authors proposed Wavelets based method for license plate registration and this is a good approach for still images.
Thesis Objectives
Again increased mobility and internationalization pose the challenge of developing an effective LPD system that can handle tiles from different countries with different character sets and syntax. So, there are still many areas in conducting research to develop a robust, accurate and country-independent LPD system.
Outline of the Thesis
Introduction
Image Description
Types of Image
Each pixel of a raster image is usually associated with a specific 'position' in some 2D region and has a value consisting of one or more quantities (patterns) associated with that position. The color used for the objects in the image is the foreground color and the rest of the image is the background color. A grayscale digital image is an image in which the value of each pixel is a single sample, carrying only intensity information.
Grayscale images are also called monochromatic, indicating the absence of any chromatic variation (ie: no color). Grayscale images are often the result of measuring the light intensity at each pixel in a single band of the electromagnetic spectrum (eg infrared, visible light, ultraviolet, etc.), and in such cases they are proper monochromatic when only one frequency data is captured.
RGB Color Model
Gray Level
Binary Level
Histogram / Histogram Distribution
In Figure 2.2, the bars "peak" at approx. 70 and 110, which indicates that these shades of gray occur most frequently in the image. The first two masks change the input image in vertical or horizontal direction, while the other two work in both directions. It can be seen that there are two very bright spots in the Radon transform, and the positions show the parameters of the lines in the original image.
FRAT maps an image of size × in the image domain to a coefficient matrix of × (+1), where the -th column represents the FRAT coefficient set of the corresponding slope. We assume that the optimal ordering FRAT is adopted in this paper in the remainder, where it is considered as an index in the set of optimal FRAT normal vectors rather than as a slope value.
Finite Ridgelet Transform - FRIT
The Haar transform
In this section, we will introduce the basic concepts related to the Haar transform, which will be explored in more detail in the following sections. First, we need to define the type of signals that we will analyze with the Haar transform. In general, we will express a discrete signal in the form ƒ = (ƒ1, ƒ 2, . . . , ƒN), where N is a positive even integer, which we will call the length f.
For simplicity, we will assume that the time increment separating each pair of consecutive time values is always the same. We will use the expression equally spaced sample values, or simply sample values, when the values of the discrete signal are defined in this way.
Geometric Mean
Continuing in this manner, all values of a1 are produced by taking the means of successive pairs of values and then multiplying these means by √2.
Chapter Summary
License Plate Detection
Principles of Number Plate Detection
For this reason, it is necessary to find an alternative definition of a license plate based on machine-understandable descriptors. If we define a number plate by its color, a specific shape (e.g. a rectangle with a 2:1 aspect ratio) or the type of text it contains, most of the problems discussed earlier would arise and the technique would not be effective in the general sense. On the other hand, if we define the license plate with edge features, we can bypass most of the problems associated with the license plate detection technique.
For example, we can think of a license plate by a region bounded by edges of a rectangular type of shape that contains many horizontal and vertical edges due to the text in it. If we think like this, we don't need to worry about any other features of a license plate. Moreover, there is no impact on the license plate detection where the license plate is rotated/skewed on the image, if we are concerned about edge analysis.
That's why we defined the license plate as a “rectangular area with an increasing number of horizontal and vertical edges.” The high density of horizontal and vertical edges on a small area is in many cases caused by contrasting characters of a license plate, but not in all cases. As a result, this technique allows us to detect different regions of interest (RoI) for the plate, and then we choose the best one through further analysis.
This function is always discrete on digital computers, such as , denoting the set of natural numbers, including zero. The modified image is then thresholded using the histogram thresholding method and a series of morphological operations are then performed.
License Plate Detection Using FRIT
Steps for FRIT Based Thresholding
Work Flow of the Proposed Technique
In summary, for this threshold value, all the significant FRIT coefficients are subtracted to zero according to the threshold values and others remain intact. The result is an abstract image of the original input image, where there are strong large and small edges. Here we have stated the steps of the proposed technique for license plate detection using FRIT.
License Plate Localization and detection
Figure: 3.6: Regions of interest on the left and verified number plates for original input images on the right. i) Input Image ii) Converted Gray Image . iii) Image after applying FRIT iv) Thresholded Image .. v) Image output after performing morphological operations and verifications. Locating and detecting a license plate consists of these several steps, and finding the areas of interest is a key step. After finding the RoI, we can verify this region(s) to ensure it is a license plate using the methods mentioned above.
By filtering them we were able to find the correct region for license plate and also find multiple regions for multiple license plates.
Chapter Summary
Results and Discussion
Introduction
Simulation Results and Comparison with Other Systems
- Overall Performance
- MATLAB based simulation results
- Performance and Time Comparison with Other Systems
In the process, there are many places where we can optimize and make this system more robust. To detect the license plate of all sizes, we first imposed no restrictions on the license plate size, but from the literature discussed earlier, we found that very small license plates are useless for the AVNPR system as they are poor for character segmentation. Below we represent significant findings and results of this newly proposed method and comparison of the proposed method with other existing methods.
The input images were of different types, such as blurred image, image containing image of dirty and dusty cars, image of multiple cars, low contrast image, etc. The output is always the license plate, but lower brightness, lower contrast and higher contrast means that technique is very more effective for a poorly lit input image. a) Original image,. b) Rotated image. However, after enhancing the input image, the number plates were able to detect using the proposed technique.
In most of the existing methods discussed in the literature, it is very difficult if there is a shadow on the license plate. It is almost impossible with the methods discussed in the literature, but as we can see, our technique here is much more effective. a) Input image. Most of the existing techniques discussed in the literature suffer from being unable to detect license plates with a blurry input image in most cases.
This example shows that our technique does not require image enhancement of input images due to blur, although image enhancement would improve the performance of the technique. a) Input and output image. f) Input and output image Figure 4.5: Effects on blurred image. From the above result as shown in Table 4.3, we can see that our proposed method can successfully detect the license plate in different working conditions.
Chapter Summary
Conclusion
Conclusion
Ioannis and others, "License Plate Recognition from Still Images and Video Sequences: A Survey", IEEE Transactions on Intelligent Transportation Systems, vol Boroujeni, "Design and Implementation of Automatic Vehicle License Plate Image Capture System", Master's Thesis, Amirkabir University of Technology (Tehran Polytechnic), 2000. Changping, “A hybrid number plate extraction method based on edge statistics and morphology”, 17th International Conference on Pattern Recognition, 2004.
Chen, “Morphology-based license plate detection from complex scenes”, 16th International Conference on Pattern Recognition, 2002. Kim, “A robust license plate extraction method under complex image conditions”, 16th International Conference on Pattern Recognition, 2002. Wan, “Car license plate detection based on MSER”, International Conference on Consumer Electronics, Communications and Networks, pp.
Kunfeng, “A License Plate Recognition System Based on Analysis of Maximum Stable Extremal Regions”, 9th IEEE International Conference on Networking, Sensing and Control, 2012. Kumudha, “License Plate Recognition- A Template Matching Method”, International Journal of Engineering Research and Applications (IJERA ) full. Junxi, “License plate detection algorithm based on soft AdaBoost algorithm with a cascade structure,” IEEE International Conference on Robotics and Biometrics, 2009.
Hernsoo, “License Plate Recognition Method Using Vertical Boundary Pairs and Geometric Relationships,” Computer Engineering and Technology (ICCET), 2010. Pengfei, “License Plate Recognition Algorithm Applied to Intelligent Transportation System,” IEEE Transactions on Intelligent Transportation Systems, vol. .12, no. 29] Kahraman, F., Kurt, B., Gökmen, M., “License plate character segmentation based on Gabor transform and vector quantization”, vol.
APPENDIX
