‘Halal’ logo detection and recognition system.
Illegal and unapproved ‘Halal’ logo has been widely used by many unscrupulous producers on their products. Consequently, Muslim consumers become confused in deciding whether a product is carrying a legal ‘Halal’ logo or otherwise. This paper reports the use of an image detection and recognition syst...
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| நிகழà¯à®¨à®¿à®²à¯ˆ அணà¯à®•à®²à¯: | http://eprints.uthm.edu.my/2287/ http://eprints.uthm.edu.my/2287/1/618.pdf |
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| தொகà¯à®ªà¯à®ªà¯: | Illegal and unapproved ‘Halal’ logo has been
widely used by many unscrupulous producers on
their products. Consequently, Muslim consumers
become confused in deciding whether a product is
carrying a legal ‘Halal’ logo or otherwise. This
paper reports the use of an image detection and
recognition system in overcoming the problem.
This system is an essential module for the user
warning assistance and it contains two main
modules; detection and recognition module. The
images of ‘Halal’ logo were capture by using a
digital camera. The images were taken from
various product surfaces such as metal, plastic and
glass. Then ‘Halal’ logo images were detected in
order to load the images manually to the
recognition system. After doing preprocessing
process on the samples of ‘Halal’ logo images, it
shows that Gaussian Blur effect give a good
impression on the detection time. Therefore, it is
the most suitable techniques for detection system to
detect and crop ‘Halal’ image properly. From the
observation based on the result, Gaussian blur
technique state about 85.71% in successfully crop
the image compared to normal image, 19.05% and
brightness and contrast effect, 47.62%. In the
recognition system, Neural Networks methods were
used to recognize and classify the images. It is a
suitable technique in solving such complex
problems. Neural network were fed by 2500 bits of
1’s and 0’s. In order to increase the recognition
system performance, it’s depends on how the
Neural Network was trained and many sets of
binary logo should be used in the system. |
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