Automatic removal of crossed-out handwritten text and the effect on writer verification and identification

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.