01559nas a2200277   4500000000100000000000100001008004100002260001200043653002100055653003800076653001600114653002700130653003300157653002300190653002400213100001900237700002200256700001800278700001200296245009100308856009900399300001100498490000600509520075200515022001401267       2017                            d  c12/201710aArabic Documents10aHandwritten Character Recognition10aText Mining10aText Line Segmentation10aConnected Component Analysis10aProjection Profile10aWatershed Transform1 aYoussef Boulid1 aAbdelghani Souhar1 aMly. Ouagague1 aE Ameur00aSegmentation of Arabic Handwritten Documents into Text Lines using Watershed Transform  uhttp://www.ijimai.org/journal/sites/default/files/files/2017/08/ijimai20174_6_13_pdf_11239.pdf  a96-1020 v43 aA crucial task in character recognition systems is the segmentation of the document into text lines and especially if it is handwritten. When dealing with non-Latin document such as Arabic, the challenge becomes greater since in addition to the variability of writing, the presence of diacritical points and the high number of ascender and descender characters complicates more the process of the segmentation. To remedy with this complexity and even to make this difficulty an advantage since the focus is on the Arabic language which is semi-cursive in nature, a method based on the Watershed Transform technique is proposed. Tested on «Handwritten Arabic Proximity Datasets» a segmentation rate of 93% for a 95% of matching score is achieved.  a1989-1660