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

TitleHandwritten Character Recognition Based on the Specificity and the Singularity of the Arabic Language
Publication TypeJournal Article
Year of Publication2017
AuthorsBoulid, Y., A. Souhar, and M. E. Elkettani
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
ISSN1989-1660
IssueRegular Issue
Volume4
Number4
Date Published06/2017
Pagination45-53
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%.

KeywordsArabic Documents, Feature Extraction, Handwritten Character Recognition, Text Classification
DOI10.9781/ijimai.2017.446
URLhttp://www.ijimai.org/journal/sites/default/files/files/2016/12/ijimai20174_4_6_pdf_26575.pdf
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ijimai20174_4_6.pdf1.85 MB