Using perturbed handwriting to support writer identification in the presence of severe data constraints
Jin Chen, Wen Cheng, Daniel Lopresti · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
Since real data is time-consuming and expensive to collect and label, 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, adversaries cannot be expected to provide sufficient data to train a classifier. Therefore, it is unrealistic to always assume sufficient real data to train classifiers extensively 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. Building on work described in another paper where human subjects were involved in calibrating realistic-looking transformation, we then measured the effects of incorporating perturbed handwriting into the training dataset. Experimental results justified our hypothesis that with limited real data, model-perturbed handwriting improved the performance of writer identification. Particularly, if only one single sample for each writer was available, incorporating perturbed data achieved a 36x performance gain.