Offline recognition of omnifont Arabic text using the HMM ToolKit (HTK)
Pattern Recognition Letters
Arabic Handwriting Recognition Competition
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Recognition of off-line printed Arabic text using Hidden Markov Models
Signal Processing
Duration Models for Arabic Text Recognition Using Hidden Markov Models
CIMCA '08 Proceedings of the 2008 International Conference on Computational Intelligence for Modelling Control & Automation
Affixal Approach versus Analytical Approach for Off-Line Arabic Decomposable Vocabulary Recognition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
ICDAR 2009 Arabic Handwriting Recognition Competition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
ICDAR 2009 Online Arabic Handwriting Recognition Competition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
A New Arabic Printed Text Image Database and Evaluation Protocols
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Gaussian Mixture Models for Arabic Font Recognition
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Impact of Character Models Choice on Arabic Text Recognition Performance
ICFHR '10 Proceedings of the 2010 12th International Conference on Frontiers in Handwriting Recognition
Pattern Recognition Letters
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A known difficulty of Arabic text recognition is in the large variability of printed representation from one font to the other. In this paper, we present a comparative study between two strategies for the recognition of multi-font Arabic text. The first strategy is to use a global recognition system working independently on all the fonts. The second strategy is to use a so-called cascade built from a font identification system followed by font-dependent systems. In order to reach a fair comparison, the feature extraction and the modeling algorithms based on HMMs are kept as similar as possible between both approaches. The evaluation is carried out on the large and publicly available APTI (Arabic Printed Text Image) database with 10 different fonts. The results are showing a clear advantage of performance for the cascading approach. However, the cascading system is more costly in terms of cpu and memory.