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Vol. 10, No. 4, November 2025 (Article in Progress)

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Implementation of Convolutional Recurrent Neural Network for Vehicle Number Plate Identification in Raspberry Pi Based Parking System

https://doi.org/10.22219/kinetik.v10i4.2320
Rivaul Muzammil
Universitas Syiah Kuala
Maulisa Oktiana
Universitas Syiah Kuala
Roslidar
Universitas Syiah Kuala

Corresponding Author(s) : Rivaul Muzammil

muzzammil.rm@gmail.com

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 10, No. 4, November 2025 (Article in Progress)

Abstract

The increasing number of vehicles in Indonesia has posed significant challenges in the management of parking facilities. This study proposes the development and implementation of an intelligent parking system based on automatic vehicle license plate character recognition. The proposed system employs the You Only Look Once version 8 (YOLOv8) model to detect the license plate region, and a Convolutional Recurrent Neural Network (CRNN) to recognize the alphanumeric characters contained within the plate. The system architecture integrates a Raspberry Pi, a camera module, and a servo motor to facilitate the automatic detection and recognition of license plates as vehicles enter and exit parking areas. The YOLOv8 model is responsible for identifying the license plate region by generating a bounding box through a convolutional layer, which is then used to isolate the license plate area from the original image. This cropped image undergoes a pre-processing stage to conform with the input specifications of the CRNN model. Subsequently, the CRNN model extracts visual features through convolutional layers and leverages recurrent layers to capture the sequential relationship among the characters on the license plate. The entire processing pipeline is deployed on the Raspberry Pi using TensorFlow Lite, ensuring efficient operation of both the YOLOv8 and CRNN models in a resource-constrained environment. Experimental results demonstrate that the YOLOv8 model achieved a detection accuracy of 94.69% for license plate localization, with a precision of 98.32%, recall of 96.25%, and an F1-score of 97.27%. In parallel, the CRNN model attained a character recognition accuracy of 93.8% across a test set comprising 30 license plates. Nevertheless, the system encountered some recognition errors, such as misclassification of the character 'G' as 'C', 'W' as 'H', and 'Q' as 'O'.

Keywords

Parking system License plate numbers You Only Look Once Convolutional Recurrent Neural Network Raspberry Pi
Muzammil, R., Oktiana, M. ., & Roslidar. (2025). Implementation of Convolutional Recurrent Neural Network for Vehicle Number Plate Identification in Raspberry Pi Based Parking System. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 10(4). https://doi.org/10.22219/kinetik.v10i4.2320
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