A COMPARISON PRE-TRAINED MODELS FOR AUTOMATIC INDONESIAN LICENSE PLATE RECOGNITION

Sahid Bismantoko (Center of Technology for System and Infrastructure of Transportation, Agency for the Assessment and Application of Technology)
M. Rosyidi (Center of Technology for System and Infrastructure of Transportation, Agency for the Assessment and Application of Technology)
Umi Chasanah (Center of Technology for System and Infrastructure of Transportation, Agency for the Assessment and Application of Technology)
Asep Haryono (Center of Technology for System and Infrastructure of Transportation, Agency for the Assessment and Application of Technology)
Tri Widodo (Center of Technology for System and Infrastructure of Transportation, Agency for the Assessment and Application of Technology)

Majalah Ilmiah Pengkajian Industri
Vol. 15 No. 1 (2021): Majalah Ilmiah Pengkajian Industri

Automatic License Plate Recognition is related to the Intelligent Transportation System (ITS) that supports the road's e-law enforcement system. In the case of the Indonesian license plate, with various colour rules for font and background, and sometimes vehicle owners modify their license plate font format, this is a challenge in the image processing approach. This research utilizes pre-trained of AlexNet, VGGNet, and ResNet to determine the optimum model of Indonesian character license plate recognition. Three pre-trained approaches in CNN-based detection for reducing time for a build if model from scratch. The experiment shows that using the pre-trained ResNet model gives a better result than another two approaches. The optimum results were obtained at epoch 50 with an accuracy of 99.9% and computation time of 26 minutes. This experiment results fulfil the goal of this research. Keywords : ALPR; ITS; CNN; AlexNet; VGGNet; ResNet