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.
@article{arxiv.1411.3519,
title = {Window-Based Descriptors for Arabic Handwritten Alphabet Recognition: A Comparative Study on a Novel Dataset},
author = {Marwan Torki and Mohamed E. Hussein and Ahmed Elsallamy and Mahmoud Fayyaz and Shehab Yaser},
journal= {arXiv preprint arXiv:1411.3519},
year = {2014}
}