This paper presents an innovative approach to license plate recognition (LPR) by integrating Convolutional Neural Network (CNN), Haar cascades, and Optical Character Recognition (OCR). The study aims to address the challenges of robust and accurate identification of license plates in varying scenarios. Several existing works in LPR are evaluated, providing a comparative analysis of their accuracies. Notable works by XIN LI, Zahra Taleb Soghadi, Rajdeep Adak, Abhishek Kumbhar, Rajas Pathare, Sagar Gowda, and Prachi M. Nilekar are examined, revealing accuracy rates ranging from 79.30% to 95%.

The proposed approach combines the strengths of CNN for feature extraction, Haar cascades for object detection, and OCR for character recognition. Through experimental validation, the hybrid system achieves an impressive accuracy of 99%, surpassing the performance of previous works. The integration of these techniques enhances the adaptability and robustness of the system across diverse license plate scenarios.

The findings underscore the continuous evolution of LPR systems and suggest that the proposed methodology represents a promising avenue for achieving higher accuracy rates. This hybrid approach not only advances the state-of-the-art in license plate recognition but also opens avenues for practical applications in law enforcement, traffic management, and surveillance. The insights gained from this study contribute to the ongoing refinement and development of reliable and efficient license plate recognition systems in real-world scenarios.