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This method is the most simple to implement on Micro controllers as the complexity of the the equation is very simple. This method emphasize more on the match of the location of the pixels. Weights are not involve in the calculation of matching percentage but will be require during training of the net.
The result of the recognition ins base on the number sum of matching pixels divide by then total of pixel scan. To improve the performance of the method error scaling in introduce. When ever there is a mismatch, a error value with is a few times more is added. this will cause the sum of match value to decrease.
This method will not work well if the scan pixel is below
0. As the less pixel mean that each mismatch will take up more percentage. It will be every effective if it is use with image more then 100 where the weight of each pix is only 1%.
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When using this method the result will be more actual as the Error will be proportional to the number of pixel and is scale according to the value weight.
Example if your input device (camera) give you a image of resolution 100 by 80 Pix. Then the size if each character template will be of size of 8000 Bytes. The size of the weight will depend on user, the more number you can have in your weights the better your system.
A negative value in the weight template signify that the template is expecting a negative value in the input image.
Result when the input when it is compare with the Template 1 and will be
Assume that the value of A,B,C,D,E,F,G,H,I = 1
Recog % (Template 1) = {-10(A+B+G) + 10(B+D+E+F+H+I)} / Sum of Template (0) ---- Total mismatch
Recog % (Template ) = {-10(A+C+D+E+F+G+H) - 10I} / Sum of Template (0) ---- Total 1 mismatch
The value Recog % (Template 1) is 0 %
The value Recog % (Template ) is 0 %
This process is then repeated till all the templates have been compared. With enough training to the system , normally the one with the highest percentage is the one that is most similar to the input hence the letter.
Note that in real life situation the value of the weights will not be so nicely valued like the one I had. It may be of any values depending on how the user define it.
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