Handwritten Character Recognition Based on the Specificity and the Singularity of the Arabic Language
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 f...
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Universidad Internacional de La Rioja (UNIR)
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doaj-2360aaa60366426c9eb976acdebcbcfd2020-11-24T23:23:51ZengUniversidad Internacional de La Rioja (UNIR)International Journal of Interactive Multimedia and Artificial Intelligence1989-16601989-16602017-08-0144455310.9781/ijimai.2017.447ijimai.2017.447Handwritten Character Recognition Based on the Specificity and the Singularity of the Arabic LanguageYoussef BoulidAbdelghani SouharMohamed Elyoussfi ElkettaniA 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%.http://www.ijimai.org/journal/node/1515Arabic DocumentsFeature ExtractionHandwritten Character RecognitionText Classification |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Youssef Boulid Abdelghani Souhar Mohamed Elyoussfi Elkettani |
spellingShingle |
Youssef Boulid Abdelghani Souhar Mohamed Elyoussfi Elkettani Handwritten Character Recognition Based on the Specificity and the Singularity of the Arabic Language International Journal of Interactive Multimedia and Artificial Intelligence Arabic Documents Feature Extraction Handwritten Character Recognition Text Classification |
author_facet |
Youssef Boulid Abdelghani Souhar Mohamed Elyoussfi Elkettani |
author_sort |
Youssef Boulid |
title |
Handwritten Character Recognition Based on the Specificity and the Singularity of the Arabic Language |
title_short |
Handwritten Character Recognition Based on the Specificity and the Singularity of the Arabic Language |
title_full |
Handwritten Character Recognition Based on the Specificity and the Singularity of the Arabic Language |
title_fullStr |
Handwritten Character Recognition Based on the Specificity and the Singularity of the Arabic Language |
title_full_unstemmed |
Handwritten Character Recognition Based on the Specificity and the Singularity of the Arabic Language |
title_sort |
handwritten character recognition based on the specificity and the singularity of the arabic language |
publisher |
Universidad Internacional de La Rioja (UNIR) |
series |
International Journal of Interactive Multimedia and Artificial Intelligence |
issn |
1989-1660 1989-1660 |
publishDate |
2017-08-01 |
description |
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%. |
topic |
Arabic Documents Feature Extraction Handwritten Character Recognition Text Classification |
url |
http://www.ijimai.org/journal/node/1515 |
work_keys_str_mv |
AT youssefboulid handwrittencharacterrecognitionbasedonthespecificityandthesingularityofthearabiclanguage AT abdelghanisouhar handwrittencharacterrecognitionbasedonthespecificityandthesingularityofthearabiclanguage AT mohamedelyoussfielkettani handwrittencharacterrecognitionbasedonthespecificityandthesingularityofthearabiclanguage |
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