Window-Based Descriptors for Arabic Handwritten Alphabet Recognition: A Comparative Study on a Novel Dataset

TL;DR

This study evaluates various window-based descriptors for Arabic handwritten alphabet recognition, introduces a new dataset, and proposes a spatial pyramid scheme to improve recognition accuracy.

Contribution

The paper provides a comprehensive comparison of descriptors, introduces a novel dataset, and presents a new spatial pyramid partitioning method to enhance recognition performance.

Findings

Descriptors perform very well in recognition tasks.

The spatial pyramid scheme improves accuracy.

The new dataset serves as a benchmark for future research.

Abstract

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.