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4. RESULT International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.4, No.3, May 2014

Table 1 4. RESULT International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.4, No.3, May 2014

Related Figures (14)

Figure 1. Flowchart
Figure 1. Flowchart
This algorithm works on Gray level image, for pre- processing and identifying the required information. In this step coloured image is converted into the Gray scale image. Gray scale image is shown in figure 3. Figure 3. Gray Image
This algorithm works on Gray level image, for pre- processing and identifying the required information. In this step coloured image is converted into the Gray scale image. Gray scale image is shown in figure 3. Figure 3. Gray Image
International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.4, No.3, May 2014 Figure 2. Input Image
International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.4, No.3, May 2014 Figure 2. Input Image
Figure 4. Dilated Image International Journal in Foundations of Computer Science & Technology (JFCST), Vol.4, No.3, May 2014
Figure 4. Dilated Image International Journal in Foundations of Computer Science & Technology (JFCST), Vol.4, No.3, May 2014
Histogram values are passed through low pass filter because values of histogram between consecutive row and column changes drastically, to minimize loss of information smooth out changes. In this step histogram value is averaged out among both sides. This step is performed for both horizontal and vertical histograms. Filtering removes all the unwanted regions of an image. Passing histogram through low pass filter is shown in figure 5 and 6. Figure5. Vertical Edge processing
Histogram values are passed through low pass filter because values of histogram between consecutive row and column changes drastically, to minimize loss of information smooth out changes. In this step histogram value is averaged out among both sides. This step is performed for both horizontal and vertical histograms. Filtering removes all the unwanted regions of an image. Passing histogram through low pass filter is shown in figure 5 and 6. Figure5. Vertical Edge processing
International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.4, No.3, May 2014 Figure 6. Horizontal Edge Processing
International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.4, No.3, May 2014 Figure 6. Horizontal Edge Processing
Figure7. Segmented Image Image has been segmented. In this step all the regions which have probability of license plate has been identified and coordinates of such probable region has been stored. The following figure shows the segmented region. The segmented regions are shown in fig7.
Figure7. Segmented Image Image has been segmented. In this step all the regions which have probability of license plate has been identified and coordinates of such probable region has been stored. The following figure shows the segmented region. The segmented regions are shown in fig7.
Individual alphanumeric characters are segmented. Segmentation has been done by using smearing algorithms in both horizontal and vertical histogram. For filling space of inner part of each character the vertical smearing algorithm is applied and some threshold value is determined. Similarly, horizontal smearing algorithm is applied. Each individual alphanumeric character is extracted by finding starting and ending points of character in horizontal direction. These characters are shown in figure 10.
Individual alphanumeric characters are segmented. Segmentation has been done by using smearing algorithms in both horizontal and vertical histogram. For filling space of inner part of each character the vertical smearing algorithm is applied and some threshold value is determined. Similarly, horizontal smearing algorithm is applied. Each individual alphanumeric character is extracted by finding starting and ending points of character in horizontal direction. These characters are shown in figure 10.
Figure9. Binary image of extracted image Image is converted into binary image from Gray scale. Intensity change value is calculated easily as compared to Gray scale and colour image. Binary image is shown in figure 9.
Figure9. Binary image of extracted image Image is converted into binary image from Gray scale. Intensity change value is calculated easily as compared to Gray scale and colour image. Binary image is shown in figure 9.
International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.4, No.3, May 2014
International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.4, No.3, May 2014
Figure10. Extracted characters from number plate International Journal in Foundations of Computer Science & Technology (JFCST), Vol.4, No.3, May 2014
Figure10. Extracted characters from number plate International Journal in Foundations of Computer Science & Technology (JFCST), Vol.4, No.3, May 2014
For Recognization of individual alphanumeric character, template based Recognization method is used. In template based algorithm, segmented image is compared with one image which is stored in database named as template image. In both images best matched similarity is compared. This similarity is matched with statistical method correlation. The image for which the correlation coefficient for template image is maximum that image is best matched. These template images are shown in figure 11. After extracting, number plate is stored in file with complete information like characters on number plate and date on which it is extracted. This shown in figure 12
For Recognization of individual alphanumeric character, template based Recognization method is used. In template based algorithm, segmented image is compared with one image which is stored in database named as template image. In both images best matched similarity is compared. This similarity is matched with statistical method correlation. The image for which the correlation coefficient for template image is maximum that image is best matched. These template images are shown in figure 11. After extracting, number plate is stored in file with complete information like characters on number plate and date on which it is extracted. This shown in figure 12
There are few problems where system does not work, these figures are shown figure 13. In these figure either the system does not extract number plate from Gray scale due to some luminance conditions or due to problematic backgrounds.
There are few problems where system does not work, these figures are shown figure 13. In these figure either the system does not extract number plate from Gray scale due to some luminance conditions or due to problematic backgrounds.
International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.4, No.3, May 2014 Figure13. Problematic images where number plate is not extracted
International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.4, No.3, May 2014 Figure13. Problematic images where number plate is not extracted
Related topics:
Computer SciencePattern RecognizationExtraction of Vehicle Number Plates Using Mathematical Morphological Techniques
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