Paper Details

Name: Arun Kalia, A J Singh
Country: India

Year: 2025
Volume: Volume-12, Issue-2 (July-December)
Page Number: 410-417
DOI: https://doi.org/10.5281/zenodo.18149889

Vehicle Number Identification is central to in intelligent transportation systems, traffic monitoring, law enforcement, and security applications. Traditional rule-based approaches, which relied on edge detection, color segmentation, and optical character recognition (optical character deciphering), often failed under real-world conditions such as poor lighting, motion blur, and diverse plate formats. In recent developments in intelligent algorithmic models (ML) and advanced neural network techniques (DL) significantly reshaped LPR (License Plate Recognition) by introducing robust feature learning, end-to-end recognition, and real-time detection capabilities. The present study reviews and validates modern ML approaches for license plate recognition, focusing on object detection models such as You Only Look Once framework and Region-based Convolutional Neural Network for plate localization, and sequence learning methods such as Convolutional Neural Networks, Convolutional Recurrent Neural Networks, and context-aware Transformer modeless for character recognition. Furthermore, the contribution of data augmentation and Generative Adversarial Network-based synthetic image generation in enhancing robustness under challenging environments is discussed. Experimental evidence from benchmark datasets, including CCPD, AOLP, and UFPR-ALPR, confirms that ML-based approaches consistently achieve 97–99% accuracy in ideal conditions and maintain 90–95% accuracy in real-world scenarios, far outperforming traditional methods. The findings validate that intelligent algorithmic models significantly enhances both accuracy and reliability in LPR systems. The study concludes that while ML-powered models excel in robustness and real-time processing, future research should address challenges such as adverse weather conditions, multilingual license plates, and privacy concerns in automated vehicle tracking.
Keywords: Vehicle Number Identification (LPR), Automatic Number Plate Recognition (ANPR), Machine Learning, Deep Learning, You Only Look Once frame work, Convolutional Neural Network-optical character deciphering, Smart Traffic Systems.