DOI: https://doi.org/10.14710/jtsiskom.2020.13726

Rekonstruksi citra kendaraan menggunakan SRCNN untuk peningkatan akurasi pengenalan pelat nomor kendaraan

Vehicle images reconstruction using SRCNN for improving the recognition accuracy of vehicle license plate number

Department of Informatics, Universitas Ma Chung, Indonesia

Received: 24 Apr 2020; Revised: 11 Sep 2020; Accepted: 13 Oct 2020; Available online: 19 Oct 2020; Published: 31 Oct 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer under http://creativecommons.org/licenses/by-sa/4.0.

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Abstract
Low-resolution images can be reconstructed into high-resolution images using the Super-resolution Convolution Neural Network (SRCNN) algorithm. This study aims to improve the vehicle license plate number's recognition accuracy by generating a high-resolution vehicle image using the SRCNN. The recognition is carried out by two types of character recognition methods: Tesseract OCR and SPNet. The training data for SRCNN uses the DIV2K dataset consisting of 900 images, while the training data for character recognition uses the Chars74 dataset. The high-resolution images constructed using SRCNN can increase the average accuracy of vehicle license plate number recognition by 16.9 % using Tesseract and 13.8 % with SPNet.
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Keywords: SPNet; SRCNN; super resolution; Tesseract OCR; image reconstruction; license plate recognition
Funding: Universitas Ma Chung

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Section: Original Research Articles
Language : ID
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