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Isolated Multifont Arabic Character Recognition Using Fourier Descriptors
Author(s) -
Shatha M. Noor
Publication year - 2013
Publication title -
journal of al-nahrain university-science
Language(s) - English
Resource type - Journals
eISSN - 2519-0881
pISSN - 1814-5922
DOI - 10.22401/jnus.16.1.26
Subject(s) - font , character (mathematics) , arabic , computer science , artificial intelligence , subject (documents) , optical character recognition , set (abstract data type) , character recognition , natural language processing , feature (linguistics) , pattern recognition (psychology) , speech recognition , test (biology) , linguistics , mathematics , philosophy , geometry , paleontology , library science , image (mathematics) , biology , programming language
Optical Characters Recognition (OCR) has been an active subject of research since the early days of computers. Despite the age of the subject, it remains one of the most challenging and exciting areas of research in computer science. In recent years it has grown into a mature discipline, producing a huge body of work. Arabic character recognition has been one of the last major languages to receive attention. In this paper a simple and accurate method is proposed to recognize isolated Arabic characters using Fourier descriptors feature set and character’s dots information represented by number of dots and their position. Eight commonly used font styles in different font sizes were used in the test, first each font style is tested separately and found the recognition ratio is excellent, then a combination of font styles were tested; and it was found that as more font styles used the recognition ratio decrease.

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