Handwritten Character Recognition Based on the Specificity and the Singularity of the Arabic Language

Author
Keywords
Abstract
A good Arabic handwritten recognition system must consider the characteristics of Arabic letters which can be explicit such as the presence of diacritics or implicit such as the baseline information (a virtual line on which cursive text are aligned and/join). In order to find an adequate method of features extraction, we have taken into consideration the nature of the Arabic characters. The paper investigate two methods based on two different visions: one describes the image in terms of the distribution of pixels, and the other describes it in terms of local patterns. Spatial Distribution of Pixels (SDP) is used according to the first vision; whereas Local Binary Patterns (LBP) are used for the second one. Tested on the Arabic portion of the Isolated Farsi Handwritten Character Database (IFHCDB) and using neural networks as a classifier, SDP achieve a recognition rate around 94% while LBP achieve a recognition rate of about 96%.
Year of Publication
2017
Journal
International Journal of Interactive Multimedia and Artificial Intelligence
Volume
4
Issue
Regular Issue
Number
4
Number of Pages
45-53
Date Published
06/2017
ISSN Number
1989-1660
Citation Key
URL
http://www.ijimai.org/journal/sites/default/files/files/2016/12/ijimai20174_4_6_pdf_26575.pdf
DOI
10.9781/ijimai.2017.446
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