Department of Informatics, Universitas Ma Chung, Indonesia
BibTex Citation Data :
@article{JTSISKOM13726, author = {Windra Swastika and Ekky Rino Fajar Sakti and Mochamad Subianto}, title = {Rekonstruksi citra kendaraan menggunakan SRCNN untuk peningkatan akurasi pengenalan pelat nomor kendaraan}, journal = {Jurnal Teknologi dan Sistem Komputer}, volume = {8}, number = {4}, year = {2020}, keywords = {SPNet; SRCNN; super resolution; Tesseract OCR; image reconstruction; license plate recognition}, 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.}, issn = {2338-0403}, pages = {304--310} doi = {10.14710/jtsiskom.2020.13726}, url = {https://jtsiskom.undip.ac.id/article/view/13726} }
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