A License Plate Recognition and Speed Detection System - Index of

W1, B2, B3, B4, W5, B6, B7, B8, B9, B10, and B11 are all contours. W1 is the top node
in the tree. It contains all other contours. B2 contains contour W5. W5 contain all the
license plate character contours.
3. Traverse down the tree, and locate any nodes that have 3 to 6 children that are all of a
certain size and are positioned at the same height. These contours are most likely license
plate characters. Therefore, a license plate has been detected.
Once a license plate is detected, the contours are passed on to the character recognition
functions. These functions convert the images to text.
Another algorithm coded at the application layer automatically calculates the most
optimal threshold value. The intensity threshold value is used by image processing functions to
determine which pixels are changed to white and which are changed to black. . The algorithm to
set this intensity threshold value is simple. The user enters a license plate number that is
currently being viewed by the camera. The software then tries a range of threshold values to
determine which threshold value provides the closest match to the license plate entered by the
user. This threshold value is then used for all subsequent license plate detections. The algorithm
steps are given below.
1. Receive the correct license plate characters input by the user.
2. Set threshold value to 0
3. Compare license plate characters detected in image to those input by the user
4. Record how many characters match
5. Increment threshold value
6. Go to step 3 until threshold reaches 255.
7. When the previous steps are complete an array will be created such as the one shown
below.
Threshold 147 148 149 150 151 152 153 154 155
Matches 5 6 5 6 5 6 6 6 5
Fig. 45 Array of correct character matches made for a particular threshold value
8. The array contains how many character matches were made for each threshold.
59