Open AccessIndonesian License Plate Detection and Identification Using Deep Learning
Author(s) -
Nico Ricky Adytia,
Gede Putra Kusuma
Publication year - 2021
Publication title -
international journal emerging technology and advanced engineering
Language(s) - English
Resource type - Journals
ISSN - 2250-2459
DOI - 10.46338/ijetae0721_01
Subject(s) - license , computer science , artificial intelligence , convolutional neural network , segmentation , identification (biology) , optical character recognition , character (mathematics) , deep learning , process (computing) , object detection , computer vision , transfer of learning , feature extraction , pattern recognition (psychology) , image segmentation , image (mathematics) , botany , geometry , mathematics , biology , operating system
— License plate is the unique identity of the vehicle, which serves as proof of the legitimacy of the operation of the vehicle in the form of a plate or other material with certain specifications issued by the police and contains the area code, registration number and validity period and installed on the vehicle. License plates are often used in automated parking systems and vehicle identification in case of traffic violations. So, it is necessary to build a system for detection and identification of license plates. The proposed license plate detection and identification system is divided into three main processes, namely license plate detection, character segmentation, and character recognition. The detection process uses transfer learning techniques using Faster R-CNN Inception V2. The segmentation process uses traditional computer vision with morphological operations and contours extraction. Then the character recognition process uses the MobileNet V2 transfer learning technique as an architecture for character classification. The recognition accuracy compared between MobileNet V2 and TesseractOCR shows that MobileNet V2 is superior with an accuracy rate of 96%, while Tesseract-OCR has a poor accuracy of 59%. Keywords— Deep Learning, Convolutional Neural Network, License Plate Detection, Character Segmentation, Character Recognition