Fuzzy logic based vehicular plate character recognition system using image segmentation and scale-invariant feature transform

• Main Authors: Bedruz, Rhen Anjerome, Sybingco, Edwin, Quiros, Ana Riza, Uy, Aaron Christian P., Vicerra, Ryan Rhay P., Dadios, Elmer P.
• Format: text
• Published: Animo Repository 2017
• Subjects: Image segmentation; Optical character recognition; Manufacturing
• 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
• Institution: De La Salle University

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.