Robust named entity detection using an Arabic offline handwriting recognition system
Proceedings of The Third Workshop on Analytics for Noisy Unstructured Text Data
Offline handwritten Arabic cursive text recognition using Hidden Markov Models and re-ranking
Pattern Recognition Letters
Using deep morphology to improve automatic error detection in Arabic handwriting recognition
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Sparsity-based super-resolution for offline handwriting recognition
Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
Arabic handwriting recognition using structural and syntactic pattern attributes
Pattern Recognition
Offline arabic handwritten text recognition: A Survey
ACM Computing Surveys (CSUR)
Statistical script independent word spotting in offline handwritten documents
Pattern Recognition
KHATT: An open Arabic offline handwritten text database
Pattern Recognition
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Offline handwriting recognition of free-flowing Arabic text is a challenging task due to the plethora of factors that contribute to the variability in the data. In this paper, we address some of these sources of variability, and present experimental results on a large corpus of handwritten documents. Specific techniques such as the application of context-dependent Hidden Markov Models (HMMs) for the cursive Arabic script, unsupervised adaptation to account for the stylistic variations across scribes, and image pre-processing to remove ruled-lines are explored. In particular, we proposed a novel integration of structural features in the HMM framework which exclusively results in a 9% relative improvement in performance. Overall, we demonstrate a relative reduction of 17% in word error rate over our baseline Arabic handwriting recognition system.