01645nas a2200229   4500000000100000000000100001008004100002260001200043653002400055653002300079653002100102653003800123100001900161700002200180700003200202245010600234856009800340300001000438490000600448520094700454022001401401       2017                            d  c06/201710aText Classification10aFeature Extraction10aArabic Documents10aHandwritten Character Recognition1 aYoussef Boulid1 aAbdelghani Souhar1 aMohamed Elyoussfi Elkettani00aHandwritten Character Recognition Based on the Specificity and the Singularity of the Arabic Language  uhttp://www.ijimai.org/journal/sites/default/files/files/2016/12/ijimai20174_4_6_pdf_26575.pdf  a45-530 v43 aA 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%.  a1989-1660