Pixel Distribution Density based character recognition For Vehicle License Plate Siddhartha Choubey
Dr.G.R.Sinha
Associate Professor (Dept. of CSE) Shri Shankaracharya College of Engg. & Tech., Bhilai C.G.,India E-mail:
[email protected]
Professor & Head (IT) Shri Shankaracharya College of Engg. & Tech., Bhilai C.G.,India E-mail:
[email protected]
Abstract— In this paper, we present a new methodology for the image segmentation and character recognition from standard Indian number plates. This method differentiates between the characters on the basis of difference in pixel distribution density in different regions of the alphabets and numeric characters. It first recognizes character is belong to which group using euler technique[ 1,2] and which subgroup does the input belongs to and then tries recognizes the input by comparing the input with the critical regions that differentiate among the elements of the same subgroup. This method eliminates the problem of confusion between similar looking elements like C, G and T, I, 1, J etc by exploiting the small but important differences among them.
character segmentation , for character recognition it uses two multi layer perceptron NN . The work presented in [1] a hybrid fuzzy based new methodology and uses Euler method for character extraction and again fuzzy is applied for character recognition for standard plate and template matching for non-standard plate. The work presented in [14-18] does not propose any method for plate location; it supposes it is already located. It does character segmentation and give training based neural network techniques for character recognition like BP, HOPFIELD, RBF SOM, and ADALINE [19-21].
KeywordsVehicle Number Plate, Detection, Segmentation, Template matching, Pixel Distribution Density etc.
A large and growing body of literature has been investigated and published on the Artificial intelligence and specially the Automatic vehicle number plate detection and vehicle number extraction. Automatic vehicle number plate detection and vehicle number extraction field and its application attracted many researchers to search and develop systems which can process images and get the useful information from them. This task is challenging due to the diversity of plate formats and the non uniform outdoor illumination conditions during image acquisition. Several techniques have been used to implement that job such as artificial neural network, template matching and fuzzy logic. This paper uses combination of both fuzzy logic and template matching to solve the problem of automatic vehicle number plate detection and vehicle number extraction. We proposed a novel method to recognize vehicle number plate elements which are captured by any camera.
I.
III.
INTRODUCTION
At present, a vital part in transportation is played by vehicles. In recent years, the usage of vehicles has been increasing on account of population growth and human needs. Hence control of vehicles is becoming a huge problem that is hard to solve [3]. An image processing technology called License Plate Recognition (LPR) that is used to identify vehicles by their license plates is a kind of automatic vehicle identification [4]. Image acquisition, license plate detection, character segmentation and character recognition are four major phases of License Plate Recognition [5, 6]. Several areas including traffic volume control, unsupervised park monitoring, traffic law enforcement and auto toll collections on highways extensively use license plate recognition applications [7,8]. In this paper, we introduce a new algorithm for character extraction and recognition based on fuzzy logic II.
SYSTEM OVERVIEW
Input image
RELATED WORK Vehicle Number Plate Detection
A licenses plate recognition system is generally divided in to three phases [1]: image acquisition, plate Recognition (subdivided in plate localization in image (ROI), character segmentation) and character recognition. Image acquisition and plate localization are described lot in many research papers; here we discuss in brief about two, but gives new methodlogy for character segmentation and recognition In [9-13] a threshold based fuzzy logic method has been proposed for preprocessing and than uses two multilayer perceptron Neural Network for the character ecognition .AVL tree Data structure is used for localization and fuzzy logic based mean opinion score is used for
Character and Number Segmentation
Feature Extraction and recognition
Character Recognition Figure 1. System Flow Chart.
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IV.
METHODOLOGY
The flow chart of the proposed system is shown in Fig.1.The first step is vehicle number plate detection which is followed by character and number segmentation, feature extraction & recognition of the extracted feature and character recognition [1].
Figure 6(b). Image after gray scale conversion.
A. Pre-processing (Vihicle Number Plate Extraction) In pre-processing, we capure the image (Fig. 2) and convert it to gray image shown in Fig.3 and using Canny edge detection method Fig.4 is obtaiended and Fig. 5 depicts the output after vehicle number plate extraction (L).
Figure 6(c). Image after black and white conversion.
Figure 6(d). Image after noise removal. Figure 2. Captured Image.
Figure 3. Gray Scale image.
B. Removing extra area from left and right region by detection of first and last columns for each character and number The first step in segmentation process is to cutoff the background from each character and number from the license plate [B]. We use vertical scanning to detect first and last columns for each character and number before horizontal scanning as explained in algorithm .Vertical scanning is done before horizontal scanning because if skewness is present in the input image then its effect will be minimized. This improvement will reduce the error of first and last columns and rows which has been uncounted using our first approach. Vertical scanning (column by column) will be done to detect the first and last columns or each component and cut the area in between to separate the license information from background. The vertical segmentation and horizontal segmentation outputs are shown in Fig. 7 and Fig. 8.
Figure 4. Iimage obtained with canny filter.
Figure 5. Extracted Vehicle number plate.
Various preprocessing steps used are: Converting image to gray scale image, converting image to black and white image, and removing all objects smaller than 100 pixels. Results of each step in preprocessing stage are shown in Fig. 6(a) - Fig. 6(d). Figure 7. Vertical Segmentation.
Figure 6(a). Input image with noise.
Figure 8. Horizontal Segmentation.
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c=c+1 i=k+1 end count = count + 1 end while
• Algorithm 1: Vertical Segmentation input plate image k=0 c=0 while (k < last column number) r=0 /*This loop identifies the starting column of each element*/ for i = k to last column /*This loop calculates total no. of white pixels in a column*/ for row = 1 to n if pixel(row, i) = white, then r = r + 1 end if r > threshold, then i is the first column, break end r=0 /*This loop identifies the last column of each element*/ for k = i to last row /*This loop calculates total no. of white pixels in a column*/ for row = 1 to n if pixel(row , i) = white, then r = r + 1 end if r < threshold, then k is the last column, break end f [c]= image between i and k c=c+1 end while •
After the first and last columns of the component have been detected, horizontal scanning will be done to detect the first and last rows from the result of the previous step and the whole process is described in Algorithm 2. The result of this operation is shown in Fig. 8. That contains only the characters or numbers without any extra area. This result will help to extract image features easily [1]. V.
FEATURE EXTRACTION
Features extraction is the step where specific information will be extracted from each character or number image which resulted from segmentation step. This operation contains the following steps: First step is to calculate the Euler number for each character and number image. The Euler number is equal to the number of connected elements (always equal to one) minus the number of holes. Three groups will result from this step as follows: • Euler number equal to 1 - This group contains C, E, F, G, H, I, J, K, L, M, N, S, T, U, V, W, X, Y and Z. It also contains the numbers 1, 2, 3, 5 and 7. • Euler number equal to 0 - This group contains A, D, O, P, Q and R and it contains number 4, 6 and 9. • Euler number equal to -1 - This group only contains character B and number 8.
Algorithm 2: Horizontal Segmentation
input f[ ] and number of images count = 0 while( count < number of images) k=0 while (k < last row number) r=0 /*This loop identifies the starting row of each element*/ for i = 1 to last row /*This loop calculates total no. of white pixels in a row*/ for column=1 to n if pixel(i ,column) = white, then r = r + 1 end if r > threshold, then i is the first row, break end r=0 /*This loop identifies the last row of each element*/ for k = i to last row /*This loop calculates total no. of white pixels in a row*/ for column = 1 to n if pixel(i , column) = white, then r = r + 1 end if r < threshold, then k is the last row, break end c=0 f [c]= image between i and k
It is clear that this grouping process will increase system accuracy of recognizing characters and numbers and it will also reduce the time of recognition process [1]. VI.
CHARACTER AND NUMBER RECOGNITION
Here we use a new methodology in which the characters are separated into subgroups on the basis of their similarity or different physical appearance. A. Division of Euler Group into sub-groups •
Euler group 1 is divided into the following subgroups: Subgroup C: consists of C and G; Subgroup E: Consists of E and F; Subgroup K: Consists of K, X and Y; Subgroup I: Consists of I, T, J and 1; Subgroup S: Consists of 5and S. • Euler group 2 is divided into the following subgroups: Subgroup O: consists of O and Q; Subgroup P : consists of P and R. All rest elements are not categorized into any subgroup and have a separate template for each of them.
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B. Deciding a group/sub-group to Character belongs Each group has a template; the template that matches with the input the most decides the group of the character. The subgroup is decided on the basis of indexes calculated by formulae: Index of a particular template = (Number of pixels that matched with that template) / (Total number of pixels in that template). The subgroup whose template has highest index with respect to the character is chosen as the subgroup.
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[2]
[3]
[4]
C. Recognition of Elements belonging to same Sub-group Once, we get the subgroup then the physical characteristics of each element that differentiate it from other elements are used to identify the element. For example:In Subgroup E: E and F are members of this subgroup. The template shown in Fig. 9(a) is used to check, if the input belongs to this group. Then the template shown in Fig. 9(b) is used to detect if the input is E or F as this template is a part of E but not F.
[5]
[6]
[7]
[8] (a) To judge group
(b) To judge whether E or F [9]
Figure 9. Template for judgement.
Similar methods based on the features characteristics of elements can be used to differentiate between different elements belonging in a subgroup. The total number of pixels in the upper half of I is almost equal to that of lower half,but in the case of T is 60% more than that in the lower half,this feature is used to differentiate betweem I & T.Thus the overall,efficiency of the system is increased by implementing this fuzzy logic. VII.
[10]
[11]
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CONCLUSIONS AND FUTURE WORK
In this paper, we have proposed a new method for character recognition which very effectively eliminates the problem of confusion between characters of same type like C & G which the previous algorithms were not able to do with high precision. Initial experiments have shown success in differentiating between similar looking characters. The above mentioned method inspite of being very effective on standard number plate suffers with the disadvantage that it is not usefull on those plate which use non-standard characters to represent the vehicle’s registration number. Ways to implement the above mentioned character recognition algorithm on non-standard vehicle number plates are still to be found. Also,the above mentioned algorithm doesnot gives optimum result when skewness is present in the detected vehicle number plate,the efficiency of the vehicle number plate detection algorithm used in this paper limits the efficiency of the total system.More work has to be done to increase its efficiency.
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