Automatic detection of handwriting forgery

We investigated the detection of handwriting forgery by both human and machine. We obtained experimental handwriting data from subjects writing samples in their natural style and writing forgeries of other subjects' handwriting. These handwriting samples were digitally scanned and stored in an image database. We investigated the ease of forging handwriting, and found that many subjects can successfully forge the handwriting of others in terms of shape and size by tracing the authentic handwriting. Our hypothesis is that the authentic handwriting samples provided by subjects in their own natural writing style will have smooth ink traces, while forged handwritings will have wrinkly traces. We believe the reason for this is that forged handwriting is often either traced or copied slowly and is therefore more likely to appear wrinkly when scanned with a high-resolution scanner. Using seven handwriting distance features, we trained an artificial neural network to achieved 89% accuracy on test samples.