Year: 2025
Type: article
Abstract: The rapid growth of urban transportation networks has increased the need for automated vehicle identification systems to support traffic management, law enforcement, and public safety. This paper presents a robust Automatic License Number Plate Recognition (ALNPR) framework that uses OpenCV for image processing and PyTesseract for character extraction. The system processes both video and image inputs to detect license plates, extract alphanumeric data, and store it in an SQLite database with timestamps. Emphasis is placed on real-time performance, modular architecture, and adaptability to different environmental conditions. The proposed method demonstrates an average recognition accuracy of 80–90% under ideal conditions and offers flexibility for integration with traffic systems. Keywords— Automatic Number Plate Recognition, OpenCV, Optical Character Recognition, PyTesseract, Intelligent Transport Systems, Computer Vision, OCR
DOI: 10.55041/ijsrem51094
Author: Humera Naaz
Institution: Jawaharlal Nehru Technological University, Hyderabad
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4411759491