Combination of HMM-based classifiers for the recognition of Arabic handwritten words

In this paper we present a two-stage system for the off-line recognition of cursive Arabic handwritten words. The proposed method is analytic without segmentation, and is able to cope with handwriting inclination and with shifted positions of diacritical marks. First, the recognition stage relies on 3 classifiers based on hidden Markov modelling (HMM). The second stage depends on the combination of these classifiers. The feature vectors used for recognition are related to pixel density distribution and to local pixel configurations. These vectors are extracted on word binary images by using a sliding window approach with different angles. We have experimented different combination schemes. The neural network-based combined system yields best performance on the IFN-ENIT benchmark data base of handwritten names of Tunisian villages/towns.