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All Journal International Journal of Electrical and Computer Engineering International Journal of Informatics and Communication Technology (IJ-ICT) Proceedings of Annual International Conference Syiah Kuala University - Life Sciences & Engineering Chapter TELKOMNIKA (Telecommunication Computing Electronics and Control) Kitektro Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Cyberspace: Jurnal Pendidikan Teknologi Informasi Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknik Informatika (JUTIF) Green Intelligent Systems and Applications Jurnal Rekayasa elektrika Jurnal Polimesin Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Pengabdian Rekayasa dan Wirausaha
. Roslidar
Electrical Engineering Department, Syiah Kuala University
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Materials Science & Nanotechnology Mechanical Engineering Other

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Implementation of Convolutional Recurrent Neural Network for Vehicle Number Plate Identification in Raspberry Pi Based Parking System Muzammil, Rivaul; Oktiana, Maulisa; Roslidar, Roslidar
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2320

Abstract

The rapid growth of vehicles in Indonesia has created significant challenges in managing parking facilities. To address this issue, this study proposes an intelligent parking system based on automatic license plate character recognition. The system employs YOLOv8 (You Only Look Once) for license plate region detection and CRNN (Convolutional Recurrent Neural Network) for alphanumeric character recognition. Its architecture integrates a Raspberry Pi, camera module, and servo motor to enable automated license plate detection and recognition during vehicle entry and exit. YOLOv8 generates bounding boxes to isolate license plate regions, which are then processed as input for CRNN. The CRNN extracts visual features through convolutional layers and captures sequential relationships among characters using recurrent layers. The entire pipeline is deployed on Raspberry Pi with TensorFlow Lite to ensure efficient computation in resource-constrained environments. Experimental results demonstrate that YOLOv8 achieved a detection accuracy of 94.69%, with a precision of 98.32%, recall of 96.25%, and F1-score of 97.27%, while CRNN reached a character recognition accuracy of 93.8% across 30 license plates. Although some recognition errors occurred, such as misclassifying ‘G’ as ‘C’, 'W' as 'H', and 'Q' as 'O', the proposed system proved effective and feasible for embedded smart parking applications.
Co-Authors . Nasaruddin A'yuni, Qurrata Akhyar Akhyar Alfatirta Mufti Alfatirta Mufti Alhamdi, Muhammad Jurej Aulia Rahman Aulia, Niza Damayanti, Elok Fathurrahman Fathurrahman Fitri Arnia Fitri Arnia Haris Akbarsyah Hendra Hidayat Indra Sakti Ira Devi Sara Islamy, Fajrul Khairul Munadi Melinda Melinda Mohd. Syaryadhi Muchtar, Kahlil Muhammad fauzan Muhammad Irhamsyah Muhammad Rizky Syahputra Mulkan Azhary Muzammil, Rivaul Nasaruddin Nasaruddin Nasaruddin Syafie Nasution, Khairunnisa Nurbadriani, Cut Nanda Oktiana, Maulisa Ridha, Jamalur Riki Effendi Riswan, Muhammad Rizal Munadi Rusdha Muharar Saddami, Khairun Safrizal Razali Safwan Syahrial, Syahrial Syukriyadin, Syukriyadin T. M. Rizki Aldiza Teuku Radhi Muhammad Fitrah Yazid Yaskur Yudha Nurdin Yudha Nurdin Yunidar