Multi-level arabic handwritten words recognition

Abstract

In this paper, we present a strategy of Arabic words recognition by combining two levels which are based on global and analytical approaches according to the topological properties of Arabic handwriting. In the first level (global), we consider the visual indices which can be generated by: diacritics and strokes (denoted tracing) that form the main shapes of the word. Each word is described as a sequence of visual indices which is treated by a "global" classifier based on Hidden Markov Model (HMM). In the second level, the word is segmented into graphemes, then each grapheme is transformed into a HMM observation by a vector quantization phase. An analytical HMM is developed in order to manage the observation sequences. At this level the diacritics are not taken in consideration which allows to reduce the number of estimated character models. Finally we combine the two approaches to decide on the class of an unknown word. In fact, the global model serves as a filter. It produces a set of hypotheses to the analytical model, which in turns, defines and outputs the final decision.

Comparative Study of Two Segmentation Methods of Handwritten Arabic Text - MM-OIC and HT-MM

“…The Arabic script is generally represented in the form of pieces or parts of words called Pieces of Arabic Words (PAWs) (Miled et al, 1998). Each PAW represents a connected component.…”