Design and Implementation of a Real-Time ANPR-OCR Based Embedded System for Automated Vehicle Compliance Verification-Checking Valid PUC and Insurance

Year : 2025 | Volume : 15 | Issue : 02 | Page : 1 7
    By

    Himanshu Lohiya,

  • Kanishka Sharma,

  • E. Rajesh,

  1. Student, Department of Computer Science and Engineering Galgotias University Greater Noida, Uttar Pradesh, India
  2. Student, Department of Computer Science and Engineering Galgotias University Greater Noida, Uttar Pradesh, India
  3. Student, Department of Computer Science and Engineering Galgotias University Greater Noida, Uttar Pradesh, India

Abstract

With the rapid increase in vehicle ownership, ensuring adherence to traffic regulations has become essential, particularly concerning vehicle insurance and Pollution Under Control (PUC) certification. Non-compliance with these requirements can lead to financial risks, legal violations, and environmental damage. Traditional enforcement methods rely on manual inspections, which are time-consuming, inefficient, and prone to human error. To address these challenges, this paper proposes an automated system that utilizes Automatic Number Plate Recognition (ANPR) and Optical Character Recognition (OCR) to monitor and enforce compliance in real time. The system captures images of vehicles using strategically placed CCTV cameras. Advanced image processing techniques detect and extract the vehicle’s registration number, which is then processed through an OCR system to convert it into text. The extracted data is cross-referenced with a government database to verify the validity of the vehicle’s insurance and PUC certification. If any discrepancies are detected, such as an expired or missing certificate, the system automatically notifies the relevant authorities and forwards the violation details to the Regional Transport Office (RTO) for further action. This leads to an automated generation and issuance of e-challans to the registered vehicle owner. By reducing the need for manual intervention, this system enhances the efficiency of traffic law enforcement while ensuring fairness and transparency in the process. The real-time nature of the system allows for immediate identification of non-compliant vehicles, contributing to improved road safety and pollution control. Additionally, the system maintains a database of repeat offenders to support stricter enforcement policies. This technology-driven approach not only streamlines compliance verification but also fosters a culture of accountability among vehicle owners.

Keywords: E-Challan, automatic number plate recognition (ANPR), traffic violation detection, image processing, optical character recognition (OCR), compliance enforcement

[This article belongs to Journal of Instrumentation Technology & Innovations ]

How to cite this article:
Himanshu Lohiya, Kanishka Sharma, E. Rajesh. Design and Implementation of a Real-Time ANPR-OCR Based Embedded System for Automated Vehicle Compliance Verification-Checking Valid PUC and Insurance. Journal of Instrumentation Technology & Innovations. 2025; 15(02):1-7.
How to cite this URL:
Himanshu Lohiya, Kanishka Sharma, E. Rajesh. Design and Implementation of a Real-Time ANPR-OCR Based Embedded System for Automated Vehicle Compliance Verification-Checking Valid PUC and Insurance. Journal of Instrumentation Technology & Innovations. 2025; 15(02):1-7. Available from: https://journals.stmjournals.com/joiti/article=2025/view=209920


References

  1. Bhardwaj D, Mahajan S. Review paper on automated number plate recognition techniques. Int. J. Emerg. Res. Manag. Technol. 2015;4:319-24.
  2.  Bailmare SH, Gadicha AB. A review paper on Vehicle Number Plate Recognition (VNPR) using improved character segmentation method. International Journal of Scientific and Research Publications. 2013 Dec;3(12):1-3.
  3.  Aalsalem MY, Khan WZ, Dhabbah KM. An automated vehicle parking monitoring and management system using ANPR cameras. In2015 17th International Conference on Advanced Communication Technology (ICACT) 2015 Jul 1 (pp. 706-710). IEEE.
  4.  Srinath R, Vrindavanam J, Sumukh YR, Yashaswini L, Chegaraddi SS. Smart Vehicle Recognition And E-Challan Generation System. In2020 International Conference for Emerging Technology (INCET) 2020 Jun 5 (pp. 1-4). IEEE.
  5.  Rogers J, Trafalgar G. Assessment of the pollution under control program in India and recommendations for improvement. The World Bank. 2002 Oct 1.
  6.  Castello P, Coelho C, Del Ninno E, Ottaviani E, Zanini M. Traffic monitoring in motorways by real-time number plate recognition. InProceedings 10th International Conference on Image Analysis and Processing 1999 Sep 27 (pp. 1128-1131). IEEE.
  7.  Kanimozhi P, Harshini S, Kumar TA, Mary MJ. Enhancing smart city security: IoT-driven ANPR technology for identifying smuggling vehicles. InIET Conference Proceedings CP913 2024 Dec 1 (Vol. 2024, No. 37, pp. 366-371). Stevenage, UK: The Institution of Engineering and Technology.
  8.  World Health Organization. Good urban governance for health and well-being: a systematic review of barriers, facilitators and indicators.
  9.  Bosco G, Riccardi V, Sciarrone A, D’Amore R, Visvizi A. AI-driven innovation in smart city governance: achieving human-centric and sustainable outcomes. Transforming Government: People, Process and Policy. 2024 Nov 26.
  10. El Hansali Y, Farrag S, Yasar A, Zavantis D. Artificial intelligence based smart traffic enforcement and management system in urban areas. SSRN. September 23, 2022.doi: 10.2139/ssrn.4227717.
Regular Issue Subscription Review Article
Volume 15
Issue 02
Received 05/04/2025
Accepted 09/04/2025
Published 10/05/2025
Publication Time 35 Days
245

Login


My IP

PlumX Metrics