Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat)
Moch Taufik
Departemen Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Sultan Agung, Semarang
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Implementation of An Indonesian Vehicle License Plate Recognition System In Real-Time Using EasyOCR and Regex Pattern Validation Moch Taufik; Asep Hernandi; Muhammad Wahyu Syaiful Anaam; Andi Riansyah
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 10 No. 2 (2025)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v10i2.473

Abstract

This study presents the design and implementation of a real-time Automatic License Plate Recognition (ALPR) system specifically tailored for Indonesian vehicle plates, integrating EasyOCR, computer vision-based image preprocessing, and Regular Expression (regex) validation. The system captures images or video streams and applies a multi-level preprocessing pipeline, including grayscale conversion, Gaussian noise reduction, edge detection, and contour-based plate localization, before performing optical character recognition based on deep learning using a convolutional recurrent neural network with an attention mechanism. Post-recognition processing with regex filtering ensures strict compliance with the official Indonesian license plate format, thereby minimizing false positives and improving recognition accuracy. Experimental evaluation using real-world surveillance data achieved 75% accuracy, 100% precision, 75% recall, and an F1-score of 86%, indicating an optimal balance between detection precision and sensitivity. The system’s advantages include real-time performance, ease of deployment with open-source software, and adaptability to various lighting and environmental conditions. However, the system still shows limitations under extreme conditions such as nighttime, heavy rain, and dense traffic, where recognition accuracy tends to decrease. Therefore, future research will focus on algorithm optimization for low-light, adverse weather, and motion-blur scenarios, large-scale deployment in urban areas, and integration with AI-based vehicle tracking, positioning this system as a key enabling technology in the development of smart city infrastructure.
Co-Authors Andi Riansyah Asep Hernandi Muhammad Wahyu Syaiful Anaam