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

• Main Authors: Windra Swastika, Ekky Rino Fajar Sakti, Mochamad Subianto
• Format: Article
• Language: English
• Published: Diponegoro University 2020-10-01
• Series: Jurnal Teknologi dan Sistem Komputer
• Subjects: rekonstruksi citra; pengenalan pelat nomor; spnet; srcnn; super resolution; tesseract ocr
• Online Access: https://jtsiskom.undip.ac.id/index.php/jtsiskom/article/view/13726

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.