DocumentCode :
594817
Title :
New features for complex Arabic fonts in cascading recognition system
Author :
Slimane, Fouad ; Zayene, Oussama ; Kanoun, Slim ; Alimi, Adel M. ; Hennebert, Jean ; Ingold, Rolf
Author_Institution :
Dept. of Inf., Univ. of Fribourg (unifr), Fribourg, Switzerland
fYear :
2012
fDate :
11-15 Nov. 2012
Firstpage :
738
Lastpage :
741
Abstract :
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.
Keywords :
feature extraction; hidden Markov models; image recognition; image resolution; natural language processing; statistical analysis; text detection; visual databases; vocabulary; APTI database; Arabic printed text image database; DecoTypeNaskh; DecoTypeThuluth; DiwaniLetter; HTK toolkit; automatic recognition; cascading recognition system; complex Arabic fonts; complex fonts; hidden Markov models; morphological variability; open vocabulary mode; printed Arabic text; robust description; statistical-based feature extraction; structural-based feature extraction; ultra low resolution; Character recognition; Databases; Educational institutions; Feature extraction; Hidden Markov models; Text recognition; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ICPR), 2012 21st International Conference on
Conference_Location :
Tsukuba
ISSN :
1051-4651
Print_ISBN :
978-1-4673-2216-4
Type :
conf
Filename :
6460240
Link To Document :
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