Orclever Proceedings of Research and Development

Artificial Intelligence-Based Mobile Vehicle Entry-Exit Monitoring Application and License Plate Recognition System

Ahmet Ertan

DHL E-Commerce Türkiye

https://orcid.org/0009-0000-9182-5785

Derya Öztürk Demir

DHL E-Commerce Türkiye

https://orcid.org/0009-0008-3445-4306

Harun Hikmet Gülerarslan

DHL E-Commerce Türkiye

https://orcid.org/0009-0003-1918-0224

Gönül Beril Aksu

DHL E-Commerce Türkiye

https://orcid.org/0009-0002-7472-9972

DOI: https://doi.org/10.56038/oprd.v6i1.640

Keywords: :License Plate Recognition, OptiOptical Character Recognition, OCR, Mobile Application

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Abstract

This study proposes the development of an Optical Character Recognition (OCR)-based system designed to automatically identify and record the license plates of vehicles entering and exiting transfer hubs. The primary objective is to reduce manual labor and mitigate data entry errors commonly encountered in traditional plate registration processes, thereby enhancing the accuracy and efficiency of vehicle access monitoring. The system architecture comprises real-time image acquisition via a mobile device camera and license plate character recognition utilizing Google ML Kit. The extracted license plate data, along with corresponding timestamps, are systematically stored in a database to enable comprehensive reporting and monitoring functionalities. Through this approach, vehicle flow within transfer centers can be effectively tracked, and operational workflows can be streamlined and digitalized to improve overall process efficiency. The results obtained from the conducted pilot study not only confirm the overall functionality of the system but also demonstrate that environmental conditions and the quality of the license plate surface have a direct impact on system performance.

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