The digital cleaning of dirty and old documents and the binarization into a black/white image can be a tedious process. It is usually done by experts. In this article a method is shown that is easy for the end user. Untrained persons are able to do this task now while before an expert was needed. The method uses interactive evolutionary computing to program image processing operations that act on the document image.
A method is presented for automatically identifying and removing crossed-out text in off-line handwriting. It classifies connected components by simply comparing two scalar features with thresholds. The performance is quantified based on manually labeled connected components of 250 pages of a forensic dataset. 47% of connected components consisting of crossed-out text can be removed automatically while 99% of the normal text components are preserved. The influence of automatically removing crossed-out text on writer verification and identification is also quantified. This influence is not significant.