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Title: Arabic handwritten document preprocessing and recognition
Authors: Chammas, Edgar
Mokbel, Chafic 
Likforman-Sulem, Laurence
Affiliations: Department of Electrical Engineering 
Keywords: Hidden Markov models
Image recognition
Optical imaging
Optical reflection
Text recognition
Subjects: Image segmentation
Issue Date: 2015
Part of: 2015 13th International Conference on Document Analysis and Recognition (ICDAR)
Start page: 451
End page: 455
Conference: International Conference on Document Analysis and Recognition (ICDAR) (13th : 23-26 Aug 2015 : Tunisia) 
Arabic handwritten documents present specific challenges due to the cursive nature of the writing and the presence of diacritical marks. Moreover, one of the largest labeled database of Arabic handwritten documents, the OpenHart-NIST database includes specific noise, namely guidelines, that has to be addressed. We propose several approaches to process these documents. First a guideline detection approach has been developed, based on K-means, that detects the documents that include guidelines. We then propose a series of preprocessing at text-line level to reduce the noise effects. For text-lines including guidelines, a guideline removal preprocessing is described and existing keystroke restoration approaches are assessed. In addition, we propose a preprocessing that combines noise removal and deskewing by removing line fragments from neighboring text lines, while searching for the principal orientation of the text-line. We provide recognition results, showing the significant improvement brought by the proposed processings.
Ezproxy URL: Link to full text
Type: Conference Paper
Appears in Collections:Department of Electrical Engineering

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