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

arXiv: https://arxiv.org/abs/1411.3519 · HTML: https://mlsanity.com/paper/1411.3519/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2014.

## Built on by (most cited first, 3 of 3)

- [Classification of Handwritten Names of Cities and Handwritten Text Recognition using Various Deep Learning Models](https://mlsanity.com/paper/2102.04816/) (2021)
- [Adapting CRISP-DM for Idea Mining: A Data Mining Process for Generating Ideas Using a Textual Dataset](https://mlsanity.com/paper/2105.00574/) (2021)
- [Movement Control of Smart Mosque's Domes using CSRNet and Fuzzy Logic Techniques](https://mlsanity.com/paper/2410.18123/) (2024)

## Abstract (arXiv)

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.