Improved Feature Extraction for Vehicle Number Plate Recognition

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 7, Issue 10, October 2017)

88 Improved Feature Extraction for Vehicle Number Plate

Recognition

Ramakrishna reddy. K

1

, Anil kumar.V

2

, Dr. Nageshwara rao. D

3

1, 2, 3_{Electronics and Communication Engineering Dept, TKR College of Engineering and Technology, Affiliated to JNTU}

Hyderabad, Telangana, India

Abstract-This system is advanced in surveillance of moving vehicles at toll Plazas, parking areas and at many unauthorized places. This will automatically generate the number of a Vehicle so that it will helpful to gather the statistics of any indiscipline vehicle. This system takes a vehicle image of any angle, any size then breaks it into smaller parts to identify the vehicle number, which include Denoising, edge detection, Thresholding, segmentation and feature extraction. These parts are then analyzed to locate the exact location of number plate in the image. Once the area of the number plate (its x and y coordinates) is found then the plate is parsed to extract the characters from it. These characters are then given to the OCR module block; it will recognize the characters and convert them in text format.

Keywords- Surveillance, Denoising, Edge Detection, Thresholding, Segmentation, OCR module.

I. INTRODUCTION

Number Plate recognition is a process to identify and to punish the over speed vehicles on highways and who drove against the traffic rules at over traffic areas in the cities. The problem of Number Plate recognition is a very interesting and very useful for many traffic management systems but difficult one. It requires some complex tasks, such as Number Plate detection, Denoising, edge detection, Thresholding, segmentation and recognition from the captured one [2]. These tasks become more sophisticated when dealing with plate images taken in various inclined angles or plate images with noise. Because, this problem is usually added in real-time systems, it requires not only accuracy but also fast processing. Most Number Plate recognition applications reduce the complexity by establishing some constrains on the position, distance from the camera to vehicles, and inclined angles, by this way, the recognition rate and accuracy of Number Plate has been improved significantly and this is done by some specific features of local Number Plates such as the number of characters, the number of rows in a plate, or colors of plate

background, or the ratio of width to height of a plate [1-2, 4].

The problem of Automatic Vehicle Number Plate recognition has been studied since 1990s and many approaches were implemented such as Characteristics of boundary lines,morphology-based, texture-based. Here the proposed approach is efficient one to extract the accurate text on the number plate through the step by step process which includes, denoising through filtering, canny base edge detection, improved Thresholding, segmentation for object recognisation and finally feature extraction using the connected components. The key factors for this technique are to detect the image edges, brightness, symmetry, angles and text [1]. The block diagram for AVNP is shown below.

Fig1.Block diagram for AVNPRS

Automatic Vehicle Number Plate recognisation System consists of four modules which are

 Captured Image Library  Pre-Processing

 Canny Edge detection & Thresholding  Segmentation and feature extraction

Image Library

Edge Detection & Thresholding Pre-Processing

Segmentation & Extraction

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The fixed cameras at toll-plazas and traffic police men at traffic junctions will capture all the indiscipline and overruled vehicle number plates while they are moving and store all the images as a library. From the library one object image will send to processing block [3]. In pre-processing, removing the noise (blurriness, holes) will be done and edge detection block is used to detect the text edges and then image will undergo through optimum Thresholding, after Thresholding image will be converting as black and white image, finally we will identify the individual characters with the help of image segmentation and feature extraction [6].

II. PROBLEM IDENTIFICATION

The practical problems for the government traffic security system is identifying the unauthorized vehicles of different states having number plates in different fonts, stolen vehicles having damaged number plate and even over speed vehicles on highways covering the number plate with different stickers. Image capturing is an unexpected single movement, so the captured images may appear in different ways such as different angles fig.2, bended (damaged) number plates fig.3, degraded number plates fig.4

Fig.2 Captured image with certain angle

Fig.3 Damaged number plate

Fig.4 Degraded number plates

Above all are images are the different problem images captured by the policemen or at toll plazas by the cameras.

III. ALGORITHM DESCRIPTION

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Fig.5 Algorithm of AVNPRS

1. Pre-processing:

Images taken from camera will processed by the pre-processing module. The purpose of this module is to remove noise and to enrich the edge features. Because, detection method based on the boundary features, it will improve the successful rate of the AVNPRS detection module [1-3].

2. Edge-Detection:

After having an obtained grayscale image, we use canny base edge detection to extract the edges of a captured image.

3. Thresholding:

Basically Thresholding is a process of converting the image into a binary one. We use the Adaptive Thresholding algorithm for the binarization step. The resulted images are used as inputs for the detection module.

4.Normalization:

Normalization is a process; it changes the range of pixel intensity values and sometimes called contrast stretching. In digital signal processing, it is referred as dynamic range expansion. The purpose of dynamic range expansion is to bring the image into a range of normal or to achieve consistency in images to avoid mental distraction or fatigue.

For example, if the intensity range of the image is 50 to 180 and the desired range is 0 to 255 the process entails subtracting 50 from each of pixel intensity, making the range 0 to 130. Then each pixel intensity is multiplied by 255/130, making the range 0 to 255. Auto-normalization in image processing software typically normalizes the full dynamic range of the number system specified in the image file format.

5.Histogram:

The occurrences of each intensity value in the image are shown. Normally, it is a graph showing the number of pixels in an image at different intensity values found in that image.

6.Histogram Equalization:

Histogram equalization assigns the new intensity values of pixels in the input image so that the output image contains a uniform distribution of intensities. It improves contrast. Histogram equalization redistributes intensity distributions. If the histogram of any image has many peaks and valleys after equalization, it will shift and spread the pixels intensities. Because of this spreading, each pixel is assigned a new intensity value based on its previous intensity level. It involves three steps which are:

 Histogram Formation

 New Intensity Values calculation for each Intensity Levels

I. Histogram Formation:

The formation of histogram for image is done with the help of mat lab command imhist(X).

TAKE A PROBLEM IMAGE

CONVRT RGB TO GRAY

REMOVE NOISE (FILTERING)

HISTGRAM and its EQUALISTION IS ALL EDGES

DETECTED?

SEGMENTATION and FEATURE EXTRACTION THRESHOLDING

EDGE DETECTION THRESHOLDING

IS NUMBER DETECTED?

END EDGE DETECTION

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Fig.6 Histogram for Input Image

II. New Intensity Values calculation:

New intensity values of any image can be calculated as follows..,

 Consider maximum intensity value in an input image.  Find the running sum (or) cumulative sum of the

intensity values.

 Normalize the values from above step by dividing the total sum.

 Multiply the values from step 2 by the maximum gray level value and round off it.

 Map the gray level values to the results by using one to one correspondence.

∑ … (1)

Using above equation we can calculate the new intensity levels for any image, here N is the number of pixels in the image.

7. Connected components:

Connected component or attached element analysis is a process of identifying all the individual components present in the image. It is used to remove the noise from the image basically. The result for connected components is shown in figure8.

8. Region Properties:

It measures a set of properties for each connected component (object) in the binary image BW, which must be a logical array; it can have any dimension. The matlab command used for this is regionprops (image, properties).

9.Word Extraction:

Extraction or character segmentation is the process, which is used to separate the character from number plate [3]. After separation each row and column information. This extraction separates each and every alphabet and number on the vehicle number plate designed on horizontally decorated in a row. The extracted data is shown in figure8 as a block diagram for the better understanding and finally these characters are applied to OCR module.

10. Optical character recognition:

Optical character recognition (OCR) is a process of converting an image of text, such as scanned paper document or electronic fax file into computer-editable text [6]. The text in an image is not editable; the letters are made of tiny dots (pixels) that together form a picture of text. During OCR, the software analyzes an image and converts the pictures of the characters to editable text based on the patterns of the pixels in the image.

IV. SIMULATION RESULTS

Fig.7 Input Image

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Fig.9 Extracted objects

Fig.10 Desired data from OCR

Table.1

Number of exp’s conducted on Different vehicles

S. no

Type

of

plate

No of

Exp’s

Conducted

No of

Correctly

Detected

%η

1 Bike

8

8 99%

2 Car

7

6 92%

3 Truck

9

7 89%

V. CONCLUSION

The system performs well on various types of Number plate images, even on scratched, scaled plate images. In addition, it can deal with the cases of multiple plates in the same image, or different types of vehicles such as motorbike plates, car plates or truck plates. In this paper we approached OCR and even Connectivity, Region properties to extract exact number plate. The System performance can be improved by using better OCRs.

Future Scope:

Actually there is a problem with OCR to recognize „0‟ and „O‟, „2‟ and „z‟, „B‟ and „8‟, „b‟ and „6‟ exactly; in future to overcome this problem we need to have a look on this.

REFERENCES

[1] Riazul Islam, Kaji Fatima sharif and Satyen Biswas, „‟Automatic Vehicle Number Plate Recognistion Using Structured Elements,‟‟ in 2015 IEEE Conference on systems, process and control, ICSPC 2015,pp.978-1-4673.

[2] Sandeep singh, Beekarampaul kaur, „‟Number Plate Recognition through image using morphological algorithm‟‟in 2016 International conference on computing suistainable global development, 978-9-3805.

[3] Junaid Ali khan, Munam Ali shah, „Car Number Plate Recognisation System using Multiple Template Matching‟‟.

[4] Kai Wang, B. Babenko, and S. Belongie, “End-to-=][end scene text recognition,” in 2011 International Conference on Computer Vision, 2011, pp. 1457–1464.

[5] I. A. Ansari and R. Y. Borse, “Image Processing and Analysis,” Int. J. Eng. Res. Appl., vol. 3, no. 4.

[6] Gonzaleg.C, „Digital Image Processing.

[7] M. A. Bin and C. Lah, “Car Plate Recognition System.”

[8] K. Bhosale, J. Jadhav, S. Kalyankar, and P. R. R. Bhambare, “Number Plate Recognition System for Toll Collection,” vol. 4, no. 4, pp. 729–732, 2014.

[9] F. Mohammad, J. Anarase, M. Shingote, and P. Ghanwat, “Optical Character Recognition Implementation Using Pattern Matching,” vol. 5, no. 2, pp. 2088–2090, 2014.

AUTHOURS BIODATA

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Anil Kumar received his B.Tech degree in Electronics and Communication Engineering from Jawaharlal Nehru technological University in 2012, and he did his Master of Engineering in Communication systems from Andhra University in 2014. He got gold medal from the university level in his masters degree, currently he is working as Assistant professor in TKR College of engineering and technology, Hyd.