01683nas a2200229   4500000000100000000000100001008004100002260001200043653002100055653003800076653002600114653002900140100001900169700002200188700001800210245007900228856009600307300001000403490000600413520102000419022001401439       2018                            d  c06/201810aArabic Documents10aHandwritten Character Recognition10aLocal Binary Patterns10aModified Bitmap Sampling1 aYoussef Boulid1 aAbdelghani Souhar1 aMly. Ouagague00aSpatial and Textural Aspects for Arabic Handwritten Characters Recognition  uhttp://www.ijimai.org/journal/sites/default/files/files/2017/12/ijimai_5_1_11_pdf_70968.pdf  a86-910 v53 aThe purpose of the present paper is the recognition of handwritten Arabic characters in their isolated form. The specificity of Arabic characters is taken into consideration, each of the proposed feature extraction method integrates one of the two aspects: spatial and textural. In the first step, a modified Bitmap Sampling method is proposed, which converts the character’s images into a binary Matrix and then constructs a Mask for each class. A matching rate is used between the input binary matrix and the masks to determinate the corresponding class. In the second step we investigate the use of an Artificial Neural Network as classifier with the binary matrices as features and then the histograms of Local Binary Patterns to capture the texture aspect of the characters. Finally, the results of these two methods are combined to take into consideration both aspects at the same time. Tested on the Arabic set of the Isolated Farsi Handwritten Character Database, the proposed method has 2.82% error rate.  a1989-1660