We propose in this work an approach for automatic recognition of printed Arabic text in open vocabulary mode and ultra low resolution (72 dpi). This system is based on Hidden Markov Models using the HTK toolkit. The novelty of our work is in the analysis of three complex fonts presenting strong ligatures: DiwaniLetter, DecoTypeNaskh and DecoTypeThuluth. We propose a feature extraction based on statistical and structural primitives allowing a robust description of the different morphological variability of the considered fonts. The system is benchmarked on the Arabic Printed Text Image (APTI) database.
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