Features Modelling in Discrete and Continuous Hidden Markov Models for Handwritten Arabic Words Recognition

The arab writing is originally cursive, difficult to segment and has a great variability. To overcome these problems, we propose two holistic approaches for the recognition of the handwritten arabic words in a limited vocabulary based on the Hidden Markov Models (HMMs): discrete with wk-means and continuous. In the suggested approach, each word of the lexicon is modelled by a discrete or continuous HMM. After a series of pre-processing, the word image is segmented from right to left in succession frames of fixed or variable size in order to generate a sequence vector of statistical and structural parameters which will be submitted to two classifiers to identify the word. To illustrate the efficiency of the proposed systems, significant experiments are carried out on IFN/ENIT benchmark database.