Vote Detection on Ballots Using Thresholding and Centroid Detection Techniques

Lailatul Qadriah, Nur Hamid

Abstract


General elections are an agenda carried out to elect and determine leaders in each region. One of the important stages in the general election process is the vote-counting stage. This study aims to implement several digital image processing methods. Digital image processing plays an important role in the automatic reading of ballot papers to increase the speed of the vote-counting process. In this study, the process of reading ballot images was conducted to produce numerical data based on the coordinates of specific parts of the image. Image processing was performed using GNU Octave software, which is simple yet effective in detecting votes on ballot papers and converting them into numerical data based on centroid coordinates. This method has advantages in terms of implementation simplicity and computational efficiency. The main stages of this study include image conversion to grayscale, thresholding, black pixel detection, segmentation, centroid coordinate detection of punched ballot marks, and conversion into numerical form. In this study, 47 ballot image samples were used. The results of this study show that this method can achieve an accuracy rate of 78.7%.


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DOI: http://dx.doi.org/10.24014/sitekin.v23i1.38937

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