Models, methods and technologies for an intelligent information-analytical system for vehicle license plate recognition

Abstract

In the era of intelligent transportation systems and smart city infrastructures, automatic license plate recognition (ALPR) has become a core enabling technology for traffic management, law enforcement, parking systems and vehicle monitoring. This paper presents a comprehensive model, method and technology stack for an intelligent information-analytical system designed for recognition of vehicle number plates. Drawing on a detailed research plan and a practical implementation of a subsystem for automatic recognition, we propose a system architecture integrating vehicle image acquisition, pre-processing, plate localisation, character segmentation and optical character recognition (OCR), and data management via relational databases. The practical part utilises modern deep-learning frameworks (YOLO, PaddleOCR, OpenCV) and SQLite3 storage. Authors evaluate results from real-world scenarios, discuss performance, robustness under adverse conditions (illumination, occlusion, weather), and address reliability, scalability and deployment issues. Furthermore, review recent advances in deep-learning based ALPR systems, including detection architectures, adversarial resilience, and edge-computing trade-offs. The novelty lies in integrating a full-stack pipeline for real-time recognition within a national-scale system context, and proposing reliability-oriented design guidelines. The paper discusses the scientific and practical contributions, limitations, and future research directions.