Knowledge transfer for writer identification

Abstract

The technical literature on writer identification usually considers the best case scenario in terms of data availability, i.e., a database composed of hundreds of writers with several documents per writer is available to train the machine learning models. However, in real-life problems such a database may not be available. In this context, learning from one dataset and transferring the knowledge to other would be extremely useful. In this paper we show how to transfer knowledge from one dataset to another through a framework that uses a writer-independent approach based on dissimilarity. Experiments on five different databases under single- and multi-script environments showed that the proposed approach achieves good results. This is an important contribution since it makes it possible do deploy the writer identification system even when no data from that particular writer are available for training.