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

*Windra Swastika scopus  -  Department of Informatics, Universitas Ma Chung, Indonesia
Ekky Rino Fajar Sakti  -  Department of Informatics, Universitas Ma Chung, Indonesia
Mochamad Subianto  -  Department of Informatics, Universitas Ma Chung, Indonesia
Received: 24 Apr 2020; Revised: 11 Sep 2020; Accepted: 13 Oct 2020; Published: 31 Oct 2020; Available online: 19 Oct 2020.
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Section: Original Research Articles
Language: ID
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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.
Keywords: SPNet; SRCNN; super resolution; Tesseract OCR; image reconstruction; license plate recognition
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