  Recognizing fonts has become an important task in document analysis, due to
the increasing number of available digital documents in different fonts and
emphases. A generic font-recognition system independent of language, script and
content is desirable for processing various types of documents. At the same
time, categorizing calligraphy styles in handwritten manuscripts is important
for palaeographic analysis, but has not been studied sufficiently in the
literature. We address the font-recognition problem as analysis and
categorization of textures. We extract features using complex wavelet transform
and use support vector machines for classification. Extensive experimental
evaluations on different datasets in four languages and comparisons with
state-of-the-art studies show that our proposed method achieves higher
recognition accuracy while being computationally simpler. Furthermore, on a new
dataset generated from Ottoman manuscripts, we show that the proposed method
can also be used for categorizing Ottoman calligraphy with high accuracy.
