Automatic License Plate Recognition (ALPRON) Using Optical Character Recognition Method - Prasetyawan, Purwono; Aulia, Muhammad Athallah Cahya; Utami, Nia Saputri; Ramadhani, Uri Arta (Journal of Applied Informatics and Computing, 2025)
Manual parking systems are prone to inefficiencies and human error, especially with increasing vehicle density. This study proposes ALPRON, an automatic license plate recognition system using Optical Character Recognition (OCR) to automate motorcycle parking management. The system integrates Raspberry Pi 4, USB cameras, and Tesseract OCR to detect and recognize license plates in real-time. Performance testing was conducted under varying distances, lighting intensities, and camera angles. The results show that the system achieves a peak recognition accuracy of 98.75% at 70 cm, in bright lighting, and a 0° camera angle. These findings suggest that ALPRON is a potentially cost-effective and efficient solution for smart parking applications, particularly in controlled campus environments. While current limitations include daylight dependency and difficulty recognizing skewed angles plates, future improvements will address these through infrared support and deep learning enhancements.