Printed PAW recognition based on planar hidden Markov models

In this paper, we present an approach for connected Arabic printed text recognition using statistical models based on planar hidden Markov models (PHMM), without prior segmentation. The performance is enhanced by the use of robust features and an efficient superstate duration distribution. The approach has been tested on a vocabulary of 11 kinds of pieces of arabic word (PAW) of three characters each. The experiments have shown promising results and directions for further improvements. The recognition accuracy has proved to be of 100% even with poor and degraded texts.

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