Smart License Plate Detection System: A Web-Centric Approach with Integrated Machine Learning Capabilities

Abstract

The manual system of license plate recognition and fine management is not efficient and contains risks of errors. This paper presents SmartLPD, a web-based application that can automatically detect and manage fines by use of computer vision, and role-based access control. The system has been implemented with Tesseract OCR as a license plate recognition system [1], [2], the Spring Boot as a backend service [3] and a responsive web interface [4], [5] which supports image upload as well as live camera capture. We can have an 85-percent detection rate with processing times of less than 3 seconds, and offer citizens and government interfaces separately. The modular architecture of the system provides the reliability of features of the fallback and access security, provided by JWT-based authentication.

Introduction

The rapid increase in motor traffic has made manual license plate recognition and fine management slow, error-prone, and inefficient, often causing delays of up to 72 hours. Existing automated ALPR solutions are limited by high costs, hardware dependence, lack of integration with fine-management systems, or inadequate role separation between citizens and authorities.

SmartLPD is proposed as a comprehensive web-based platform that integrates license plate detection, fine management, and role-based access control. It allows authorities to detect license plates and manage fines, while citizens can view and pay their fines through the same system. The platform offers accessibility without the need for specialized hardware or software.

Key contributions include a web-centric architecture supporting file uploads and real-time camera capture, domain-validated authority access, combined image-processing algorithms, an adaptive installation-free interface, and full fine-management tools.

In comparison to other ALPR systems, SmartLPD provides a unified, dual-role web solution despite slightly lower recognition accuracy. A review of existing literature highlights that traditional ALPR systems focus mainly on recognition, academic works often require heavy computation or lack management features, and existing web systems do not implement proper role-based access.

SmartLPD uses a three-tier architecture: a web-based frontend, a Spring Boot backend, a MySQL database, and an ML service using Tesseract OCR. The database includes users, fines, and detection-history tables. The implementation features role-based navigation, diverse image-input methods, JWT authentication, authority email validation, and a multi-stage computer-vision pipeline using preprocessing, contour detection, OCR, and text validation.

Conclusion

SmartLPD can be used well to demonstrate the effectiveness of integrating full fine control with license plate recognition on a web-based platform. The system will offer the following to overcome the key weaknesses of existing solutions: • One online source between authorities and citizens. • Live license plate detection without special hardware. • Domain validation and Role based access control. • Uploading files and capturing cameras are some of the input methods. • Degradation with grace through fallback. The application shows how the modern web technology could be used to facilitate the use of computer vision applications without sacrificing security and access. The modular structure is open to improvement in the future, without the need to completely re-architecture the design.

References

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Copyright

Copyright © 2025 Vedant Surkar, Dr. Rohini Chavan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.