Determining the individuality of handwriting in
ancient manuscripts is an important aspect of the manuscript
analysis process. Automatic identification of writers in historical
manuscripts can support historians to gain insights into
manuscripts with missing metadata such as writer name, period,
and origin. In this paper writer classification and retrieval
approaches for multi-page documents in the context of historical
manuscripts are presented. The main contribution is a learningbased
rejection strategy which utilizes writer retrieval and support
vector machines for rejecting a decision if no corresponding
writer can be found for a query manuscript. Experiments using
different feature extraction methods demonstrate the abilities of
our proposed methods. A dedicated data set based on a publicly
available database of historical Arabic manuscripts was used and
the experiments show promising results.