# AlexU-Word: A New Dataset for Isolated-Word Closed-Vocabulary Offline Arabic Handwriting Recognition

arXiv: https://arxiv.org/abs/1411.4670 · HTML: https://mlsanity.com/paper/1411.4670/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2014.

## Built on by (most cited first, 2 of 2)

- [Qalam : A Multimodal LLM for Arabic Optical Character and Handwriting Recognition](https://mlsanity.com/paper/2407.13559/) (2024)
- [SARD: A Large-Scale Synthetic Arabic OCR Dataset for Book-Style Text Recognition](https://mlsanity.com/paper/2505.24600/) (2025)

## Abstract (arXiv)

In this paper, we introduce the first phase of a new dataset for offline Arabic handwriting recognition. The aim is to collect a very large dataset of isolated Arabic words that covers all letters of the alphabet in all possible shapes using a small number of simple words. The end goal is to collect a very large dataset of segmented letter images, which can be used to build and evaluate Arabic handwriting recognition systems that are based on segmented letter recognition. The current version of the dataset contains 25114 samples of 109 unique Arabic words that cover all possible shapes of all alphabet letters. The samples were collected from 907 writers. In its current form, the dataset can be used for the problem of closed-vocabulary word recognition. We evaluated a number of window-based descriptors and classifiers on this task and obtained an accuracy of 92.16% using a SIFT-based descriptor and ANN.