Fuzzy logic based vehicular plate character recognition system using image segmentation and scale-invariant feature transform
This paper proposes a vehicle plate optical character recognition method using scale invariant feature transform integrated with image segmentation and fuzzy logic. Image segmentation separates every character in a plate area to get the features of every character obtained. Scale Invariant Feature T...
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| Main Authors: | , , , , , |
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| Format: | text |
| Published: |
Animo Repository
2017
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| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/faculty_research/3492 https://animorepository.dlsu.edu.ph/context/faculty_research/article/4494/type/native/viewcontent/TENCON.2016.7848088 |
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| Institution: | De La Salle University |
| Summary: | This paper proposes a vehicle plate optical character recognition method using scale invariant feature transform integrated with image segmentation and fuzzy logic. Image segmentation separates every character in a plate area to get the features of every character obtained. Scale Invariant Feature Transform or SIFT on the other hand, allows the extraction of every feature of each character obtained from the plate. Fuzzy logic analyzes the features obtained from the SIFT algorithm which is proposed to detect the characters correctly. This program used MATLAB to determine the performance of the algorithm. Using the proposed algorithm, it was shown how the algorithm was effective on extracting plate character features as well as recognizing the characters in a given image. Results show that the algorithm has an accuracy of 90.75% and now ready to use for other implementation. This can be incorporated to present optical character recognition system and test its validity and accuracy for practical purposes. © 2016 IEEE. |
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