Window-Based Descriptors for Arabic Handwritten Alphabet Recognition: A Comparative Study on a Novel Dataset
This work addresses the problem of Arabic handwritten letter recognition for researchers and practitioners, but it is incremental as it focuses on comparative evaluation and dataset creation.
The paper tackled Arabic handwritten alphabet recognition by evaluating window-based descriptors and introducing a novel spatial pyramid partitioning scheme, resulting in enhanced recognition accuracy for most descriptors, and also introduced a new dataset for benchmarking.
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.