Window-Based Descriptors for Arabic Handwritten Alphabet Recognition: A   Comparative Study on a Novel Dataset
  This paper presents a comparative study for window-based descriptors on the application of Arabic handwritten alphabet recognition. We show a detailed experimental evaluation of different descriptors with several classifiers. The objective of the paper is to evaluate different window-based descriptors on the problem of Arabic letter recognition. Our experiments clearly show that they perform very well. Moreover, we introduce a novel spatial pyramid partitioning scheme that enhances the recognition accuracy for most descriptors. In addition, we introduce a novel dataset for Arabic handwritten isolated alphabet letters, which can serve as a benchmark for future research. 
Marwan Torki, Mohamed E. Hussein, Ahmed Elsallamy, Mahmoud Fayyaz,   Shehab Yaser
cs.CV