Automatic Number Plate Recognization for mix Number plate using CNN and OCR
Volume: 12 - Issue: 11 - Date: 01-11-2023
Approved ISSN: 2278-1412
Published Id: IJAECESTU167 | Page No.: 56-62
Author: Akanksha Sarwan
Co- Author: Sachin Meshram
Abstract:-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.
Key Words:-License Plate Recognition, Convolutional Neural Network (CNN), Haar Cascades, Optical Character Recognition (OCR), Image Processing, Computer Vision, Intelligent Transportation Systems, Traffic Management Surveillance, Object Detection, Deep Learning
Area:-Engineering
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