Application of YOLOv5 and EasyOCR for Odd-Even Detection and Recognition on License Plates - Candra, Alvin; Utaminingrum, Fitri; Tolle, Herman (Journal of Information Technology and Computer Science, 2025)
The increase in the number of vehicles in Indonesia is directly correlated with the escalation in traffic congestion. In addition to congestion, an indirect repercussion of vehicle proliferation is the rise in traffic accidents. The government has undertaken various measures to alleviate traffic congestion, one of which is the implementation of the odd-even system. Automatic License Plate Recognition (ALPR) can be employed to facilitate the identification of odd-even plate numbers. In this study, ALPR employs machine learning methods (K-Nearest Neighbors and Support Vector Machine) as well as deep learning techniques (YOLOv5). EasyOCR is utilized for character recognition on the license plates. Based on the test results, YOLOv5 emerges as the optimal approach for license plate detection. EasyOCR demonstrates proficient character recognition for license plates at distances less than 2 meters.