Planar Markov modeling for Arabic writing recognition: advancement state

In this paper, we show how planar hidden Markov models (PHMM) can offer great potential to solve difficult Arabic character recognition problems, especially its cursivness. A convenient architecture is defined for printed Arabic sub-words. It yields an easy solution to implement the modeling of the different morphological variations of the Arabic writing, i.e., vertical and variable horizontal linkages. A more flexible architecture, developed for Arabic handwritten words, is under test. The structure proposed presents the aptitude to absorb the variability of the manuscript. Indeed, the experiments have shown promising results and directions for further improvements. In the present paper, we describe both retained architectures, showing the applicability of the PHMM to the Arabic complexities. This is owed precisely to the definition of the PHMM, which permits to follow efficiently the natural variations in bands of the Arabic script.