This paper presents an approach for automatic detection of fingers that mistakenly appear in scans from digitized image collections. Our goal is to create a reliable detection tool that is independent from scan quality, finger sizes, direction, shape, colour and lighting conditions. Modern image processing techniques are applied for edge detection, local image information extraction, and analysis. We employed expert knowledge to determine default parameters of the algorithm, and support customized parameters for specific institutional workflows. Results for three digital collections analysis are presented. Documents with finger artefacts are identified with high reliability and validated by human visual inspection. The proposed method achieves up to 86 percent classification accuracy.