Научная визуализация, 2020, том 12, номер 1, страницы 120 - 131, DOI: 10.26583/sv.12.1.11
Image Feature Extraction of Numbers and Letters Using Matrix Segmentation
Авторы: F. Budiman1, E. Sugiarto2
Department of Computer Science, University of Dian Nuswantoro, Semarang, Indonesia2Department of Computer Science, University of Dian Nuswantoro, Semarang, Indonesia
1 ORCID: 0000-0002-8552-6778, fikri.budiman@dsn.dinus.ac.id
2 ORCID: 0000-0002-7354-0652, edi.sugiarto@dsn.dinus.ac.id
Аннотация
Classification in recognizing image of letters and numbers is useful to recognize vehicle license plates. This study aims to maximize classification accuracy value of feature extraction method using matrix segmentation. The dataset consists of 300 vehicle license plate images which have 36 classifications, 26 classes for A-Z letters image, and 10 classes for 0-9 numbers image. The research stages carried out to maximize the results of the classification using multiclass SVM-RBF nonlinear are: preparing region image of interest, image enhancement, image segmentation of letters and numbers, determining the best n value for n x n matrix segmentation, calculating total points of each segment as feature value, and determining the best value for C and γ as the value of RBF kernel parameter. The result of this study shows a maximum value of 92% classification accuracy using n = 5, γ = 0.8, and C = 15.
Ключевые слова: Matrix segmentation, feature extraction, vehicle license plate, accuracy, Support Vector Machine.
