Major cracks on a highway or bridge's concrete surface have a massive risk of damages, accompanied by less maintenance, slow detection, and handling; the worst case of the damage is the structure's total collapse, which can produce fatalities. Moreover, Indonesia's climate and geographical location contribute to a higher level of potential damage to the structure. In order to reduce the potential damage, the need for a surface crack detection system arises. This research analysed three different databases (Database A, B, and C) with different surface concrete crack types, such as early thermal contraction, plastic shrinkage, corrosion reinforcement, and non-crack images. The total images from each Database vary from 14 images for Database A, 80 images for Database B, and 4000 images for Database C. The Otsu thresholding and mathematical morphology operations such as opening, closing, dilation, and erosion with pre-processing methods were combined and produced results for each Database with classification using Euclidean distance calculation. The best results for Database A and B were 100% using combination Otsu thresholding with Laplacian operator and Laplacian of Gaussian filter and the same result for a combination of mathematical morphological operations. The best result using Database C, which had more images than Database A and B, was 80,2% using a combination of mathematical morphological operations.