Deep Learning-Enhanced Automatic License Plate Recognition: A CNN-Based Framework for Real-Time Traffic Management Systems

Abstract

This study presents a robust Automatic License Plate Recognition (ALPR) framework leveraging deep learning techniques to address persistent challenges in intelligent transportation systems. The research objectives focus on developing a scalable, real-time solution that overcomes limitations of traditional ALPR methods, including variability in lighting conditions, diverse plate formats, and complex environmental backgrounds. The proposed framework integrates Convolutional Neural Networks (CNNs) with state-of-the-art object detection algorithms, specifically employing Faster R-CNN with Inception V2 architecture for plate localization and EasyOCR for character recognition. Utilizing open-source tools including TensorFlow and OpenCV, the system was trained and validated on a dataset of 433 annotated images, achieving 95% detection accuracy at 10,000 training iterations with a localization loss of 2.78%. Real-time performance evaluation demonstrated 92% success rate in video stream processing, confirming the framework's practical applicability. The methodology encompasses comprehensive image preprocessing, advanced feature extraction, precise plate localization, and robust optical character recognition. Experimental results validate the system's effectiveness across diverse environmental conditions, including low-light scenarios and high-occlusion situations. This research contributes a cost-effective, scalable solution that significantly outperforms traditional ALPR methods, establishing a foundation for broader implementation in smart city infrastructures and intelligent transportation systems.