  The writing can be used as an important biometric modality which allows to
unequivocally identify an individual. It happens because the writing of two
different persons present differences that can be explored both in terms of
graphometric properties or even by addressing the manuscript as a digital
image, taking into account the use of image processing techniques that can
properly capture different visual attributes of the image (e.g. texture). In
this work, perform a detailed study in which we dissect whether or not the use
of a database with only a single sample taken from some writers may skew the
results obtained in the experimental protocol. In this sense, we propose here
what we call "document filter". The "document filter" protocol is supposed to
be used as a preprocessing technique, such a way that all the data taken from
fragments of the same document must be placed either into the training or into
the test set. The rationale behind it, is that the classifier must capture the
features from the writer itself, and not features regarding other
particularities which could affect the writing in a specific document (i.e.
emotional state of the writer, pen used, paper type, and etc.). By analyzing
the literature, one can find several works dealing the writer identification
problem. However, the performance of the writer identification systems must be
evaluated also taking into account the occurrence of writer volunteers who
contributed with a single sample during the creation of the manuscript
databases. To address the open issue investigated here, a comprehensive set of
experiments was performed on the IAM, BFL and CVL databases. They have shown
that, in the most extreme case, the recognition rate obtained using the
"document filter" protocol drops from 81.80% to 50.37%.
