Writer Identification in Historical Manuscripts: Analysis and Optimisation of a Classifier with an easy-to-use Implementation for Scholars from the Humanities
Several methods have been proposed for the task of writer identification for historical manuscripts. Most of these methods have been evaluated on private historical datasets only, while few have been evaluated on the recently published historical dataset provided by the Historical-WI competition at ICDAR-2017. Nevertheless, there is no thorough analysis in the literature available w.r.t. the degradation typically found in the digitized manuscripts. Furthermore, the currently proposed methods are beyond the reach of the scholars from the humanities; either because of the impracticality of the method itself, or because of the lack of an easy-to-use implementation. In this paper, we analyse a state-of-the-art method against common degradation types in historical manuscripts using images from the virtual manuscript library of Switzerland. Furthermore, we show that, by optimising a key parameter, we can enhance the performance of the method and significantly outperform the winner method of the Historical-WI competition. Finally, we demonstrate the practicality of our implementation yielding intuitively comprehensible results for direct use of scholars from the humanities.