Title :
Printed PAW recognition based on planar hidden Markov models
Author :
Ben Amara, Najoua ; Belaid, Addel
Author_Institution :
Ecole Nat. de Ingenieurs de Monastir, Tunisia
Abstract :
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
Keywords :
hidden Markov models; image segmentation; optical character recognition; probability; connected Arabic printed text recognition; efficient superstate duration distribution; pieces of arabic word; planar hidden Markov models; recognition accuracy; statistical models; Hidden Markov models; Image segmentation; Optimized production technology; Peak to average power ratio; Plasma welding; Probability distribution; Tin; Topology; White spaces; Writing;
Conference_Titel :
Pattern Recognition, 1996., Proceedings of the 13th International Conference on
Conference_Location :
Vienna
Print_ISBN :
0-8186-7282-X
DOI :
10.1109/ICPR.1996.546821
