Using perturbed handwriting to support writer identification in the presence of severe data constraints

Since real data is time-consuming and expensive to collect, label, and use, researchers have proposed approaches using synthetic variations for the tasks of signature verification, speaker authentication, handwriting recognition, keyword spotting, etc. However, the limitation of real data is particularly critical in the field of writer identification in that in forensics, enemies or criminals usually leave little amount of real data. Therefore, it is unrealistic to always assume sufficient real data for writer identification. In addition, this field differs from many others in that we strive to preserve as much inter-writer variations, but model perturbed handwriting might break such discriminability among writers. In this work, we started by conducting user studies where human subjects were involved in calibrating realistic-looking transformations. Next, we measured the effects of incorporating perturbed handwriting into the real training dataset. Experimental results justified our hypothesis that with limited real data, model perturbed handwriting improved the performance of writer identification. In addition, we justified by experiments that it was beneficial to search for better performance in the parameter subspaces.