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arabic-characters

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arabic-characters
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---
# For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
{}
---

# Dataset Card for Arabic Characters

<!-- Provide a quick summary of the dataset. -->

## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->
This dataset contains 16,800 Arabic handwritten characters, written by 60 participants. It is intended for Arabic character recognition tasks using machine learning. The dataset is split into a training set of 13,440 images and a test set of 3,360 images, with 28 Arabic characters (labeled 0–27). Each image is 32×32 pixels in grayscale, scanned at 300 dpi and preprocessed. The original source is the Arabic Handwritten Characters Dataset.


- **License:** Open Database License (ODbL)

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

- **Homepage:** https://github.com/mloey/Arabic-Handwritten-Characters-Dataset
- **Paper:** El-Sawy, A., Loey, M., & El-Bakry, H. (2017). Arabic handwritten characters recognition using convolutional neural network. WSEAS Transactions on Computer Research, 5(1), 11-19.

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

Total images: 16,800

Splits:

- **Train**: 13,440 images (80%)

- **Test**: 3,360 images (20%)

Classes (labels): 28 (Arabic letters), labeled 0–27

Image specs: PNG format, 32×32 pixels, grayscale

## Example Usage
Below is a quick example of how to load this dataset via the Hugging Face Datasets library.
```
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("randall-lab/arabic-characters", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/arabic-characters", split="test", trust_remote_code=True)

# Access a sample from the training set
example = dataset["train"][0]
image = example["image"]
label = example["label"]

image.show()  # Display the image
print(f"Label: {label}")
```

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

@article{el2017arabic,
  title={Arabic handwritten characters recognition using convolutional neural network},
  author={El-Sawy, Ahmed and Loey, Mohamed and El-Bakry, Hazem},
  journal={WSEAS Transactions on Computer Research},
  volume={5},
  pages={11--19},
  year={2017}
}