Oyma Karakterlere Sahip Dairesel Metal Cisimlerin Sınıflandırma Probleminin Görüntü İşleme Yöntemleri İle Çözümü

Abstract

In this study, two different solution ways have been developed for the problem of classification of industrial small circular metal objects on the surfaces of engraved metal. It is the first proposed solution to perform the pattern matching with XOR operator by extract the character region of the circular metal objects as a pre-process, making the model of the Daugman’s Rubber Sheet Model (DRSM) and performing feature extraction. As a result, obtained that average processing time is 69,72 milliseconds and 0,9398 accuracy rate in the first proposed solution. The second solution is the optical character recognition (OCR) on the circular metal objects that to be realized character region detection and character segmentation as a result of the Maximal Stabil Extremal Region (MSER) and Stroke Width Transform (SWT) algorithms. Character recognition realized by using the model of Convolutional Neural Network (CNN) class which is a deep machine learning approach of artificial intelligence. The character recognition problem of the circular metal objects provided at the same time solved the problem of object classification. As a result, obtained that average processing time is 1,596 second and 0,9719 accuracy rate in the second proposed solution.