Philippine license plate character recognition using faster R-CNN with inceptionV2

• Main Authors: Amon, Mari Christine E., Brillantes, Allysa Kate M., Billones, Ciprian D., Billones, Robert Kerwin C., Jose, John Anthony C., Sybingco, Edwin, Dadios, Elmer Jose P., Fillone, Alexis, Gan Lim, Laurence, Bandala, Argel A.
• Format: text
• Published: Animo Repository 2019
• Subjects: Optical character recognition devices; Automobile license plates; Neural networks (Computer science; Electrical and Computer Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1591; https://animorepository.dlsu.edu.ph/context/faculty_research/article/2590/type/native/viewcontent
• Institution: De La Salle University

This research proposes a method for automatic license plate recognition (ALPR) using the Faster R-CNN with InceptionV2 feature extractor that works in the Philippines. While there exist character recognition systems, there still remains difficulty in recognition due to different variations of Philippine license plates. By training a deep neural network in the extraction of the features in images of the different types of Philippine license plates - 1981, 2003, 2014, and others - our proposed multi-class detection system can recognize the alphanumeric characters in the license plate images. The system was tested on actual traffic images in the Philippines that contains different types of license plates, and achieved the detection rate of 90.011%, recognition rate of 93.21% and an overall accuracy of 83.895%. © 2019 IEEE.