  A prototype system for the transliteration of diacritics-less Arabic
manuscripts at the sub-word or part of Arabic word (PAW) level is developed.
The system is able to read sub-words of the input manuscript using a set of
skeleton-based features. A variation of the system is also developed which
reads archigraphemic Arabic manuscripts, which are dot-less, into
archigraphemes transliteration. In order to reduce the complexity of the
original highly multiclass problem of sub-word recognition, it is redefined
into a set of binary descriptor classifiers. The outputs of trained binary
classifiers are combined to generate the sequence of sub-word letters. SVMs are
used to learn the binary classifiers. Two specific Arabic databases have been
developed to train and test the system. One of them is a database of the Naskh
style. The initial results are promising. The systems could be trained on other
scripts found in Arabic manuscripts.
