‘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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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
2008
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/127/ http://eprints.uthm.edu.my/127/1/mohd_norazli%2Cmohd_helmy%2Camir.pdf |
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| Summary: | 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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