HISTOGRAM E QUALIZATION SMOOTHING FOR DETERMINING THRESHOLD ACCURACY ON ANCIENT DOCUMENT IMAGE BINARIZATION - Dwipayana, Mahendar; Arnia, Fitri; Musliyana, Zuhar (JOURNAL OF INFORMATICS AND COMPUTER SCIENCE, 2016)
Ancient documents are inheritance that must be preserved. The documents contain historical, scientific, social, religious information, etc. Converting ancient documents into digital image formats is one of ways to preserve the inheritance and can be stored into a computer. However, images of ancientdocuments have many blemishes caused by age, moisture, flood, etc. Therefore, special techniques are needed for those images to be restored and can improve the legibility of the ancient documentsâ images. In this study, the image restoration process uses separation of background and foreground/text on histogram equalization such as research conducted by Fitri Arnia in 2008. Through histogram equalizationimages can be seen the distribution of pixels from the intensity of black color "0" to white "1". The distribution of pixels on histogram equalization describes the curves of foreground/text and curves of background. Among the histogram curves, the determination of thresholdvalues can be done so as to clarify the foreground/text and background areas on images of ancient documents. The lowest point between the two curves is the lowest pixel (local minima) which is used as the threshold value. However, the selection of such threshold values in some cases is very difficult to determine because there are still many fluctuations in the curve at the lowest curve. Therefore, this study proposesa histogram smoothing method in the ancient documentsâ images to minimize curvature fluctuations and to determine more accurate threshold values. In this research, average filtering method is used for smoothing the histogram image. This filter successfully refines the histogram and makes the image of the restoration or binary image display the value of the ancient document image readability increases.Keywords: HistogramEqualization, Smoothing Histogram, Average Filtering, Thresholding