---
language:
- ar
configs:
- config_name: default
  data_files:
  - split: Amiri
    path: Amiri/*.csv
  - split: Sakkal_Majalla
    path: Sakkal_Majalla/*.csv
  - split: Arial
    path: Arial/*.csv
  - split: Calibri
    path: Calibri/*.csv
  - split: Scheherazade_New
    path: Scheherazade_New/*.csv
  - split: traditional_arabic_regular
    path: traditional_arabic_regular/*.csv
features:
  text:
    dtype: string
csv_options:
  delimiter: ','
  quotechar: '"'
  encoding: utf-8
tags:
- dataset
- OCR
- Arabic
- Image_To_Text
license: cc
task_categories:
- image-to-text
pretty_name: 'SAND: A Large-Scale Synthetic Arabic OCR Corpus for Vision-Language Models'
size_categories:
- 100K<n<1M
---

# SARD: Synthetic Arabic Recognition Dataset

[![Hugging Face Datasets](https://img.shields.io/badge/🤗%20Hugging%20Face-Datasets-yellow)](https://huggingface.co/datasets/riotu-lab/SAND)
[![GitHub](https://img.shields.io/badge/GitHub-Repository-blue)](https://github.com/riotu-lab/text2image)

## Overview

**SARD** (Synthetic Arabic Recognition Dataset) is a large-scale, synthetically generated dataset designed for training and evaluating Optical Character Recognition (OCR) models for Arabic text. This dataset addresses the critical need for comprehensive Arabic text recognition resources by providing controlled, diverse, and scalable training data that simulates real-world book layouts.

## Key Features

- **Massive Scale**: 2,515,082 document images containing 794.6 million words
- **Typographic Diversity**: Five distinct Arabic fonts (Amiri, Sakkal Majalla, Arial, Calibri, Scheherazade New and Traditional Arabic)
- **Structured Formatting**: Designed to mimic real-world book layouts with consistent typography
- **Clean Data**: Synthetically generated with no scanning artifacts, blur, or distortions
- **Content Diversity**: Text spans multiple domains including culture, literature, Shariah, social topics, and more

## Dataset Structure

The dataset is divided into five splits based on font name:

- **Amiri**: ~488585 document images
- **Sakkal Majalla**: ~488585 document images
- **Arial**: ~321683 document images
- **Calibri**: ~321683 document images
- **Scheherazade New**: ~572863 document images
- **Traditional Arabic**: ~321683 document images


📋 Sample Images
<div align="center">
  <table>
    <tr>
      <td><img src="https://cdn-uploads.huggingface.co/production/uploads/64e8eb21233101ed99b204c8/gwF9jkkkpzRSzrP9GCE_l.png" width="300" alt="Sample 1 - Amiri Font"/></td>
      <td><img src="https://cdn-uploads.huggingface.co/production/uploads/64e8eb21233101ed99b204c8/dsqWoCh5x31eGq-u-PqPS.png" width="300" alt="Sample 2 - Arial Font"/></td>
    </tr>
    <tr>
      <td><img src="https://cdn-uploads.huggingface.co/production/uploads/64e8eb21233101ed99b204c8/2XK9Ey6k6HSDXKXCxmVRG.png" width="300" alt="Sample 3 - Calibri Font"/></td>
      <td><img src="https://cdn-uploads.huggingface.co/production/uploads/64e8eb21233101ed99b204c8/CxKITKvc3EnDIuqnNy_bV.png" width="300" alt="Sample 4 - Scheherazade Font"/></td>
    </tr>
  </table>
</div>

Each split contains data specific to a single font with the following attributes:

- `image_name`: Unique identifier for each image
- `chunk`: The text content associated with the image
- `font_name`: The font used in text rendering
- `image_base64`: Base64-encoded image representation
- `sample_id`: Unique ID
- `article_link`: Link of the source article 

## Content Distribution

| Category | Number of Articles |
|----------|-------------------|
| Culture | 13,253 |
| Fatawa & Counsels | 8,096 |
| Literature & Language | 11,581 |
| Bibliography | 26,393 |
| Publications & Competitions | 1,123 |
| Shariah | 46,665 |
| Social | 8,827 |
| Translations | 443 |
| Muslim's News | 16,725 |
| **Total Articles** | **133,105** |

## Font Specifications

| Font | Words Per Page | Font Size |
|------|----------------|-----------|
| Sakkal Majalla | 50–300 | 14 pt |
| Arial | 50–500 | 12 pt |
| Calibri | 50–500 | 12 pt |
| Amiri | 50–300 | 12 pt |
| Scheherazade | 50–250 | 12 pt |
| Traditional Arabic | 50–500 | 12 pt |

## Page Layout

| Specification | Measurement |
|---------------|-------------|
| Left Margin | 0.9 inches |
| Right Margin | 0.9 inches |
| Top Margin | 1.0 inch |
| Bottom Margin | 1.0 inch |
| Gutter Margin | 0.2 inches |
| Page Width | 8.27 inches (A4) |
| Page Height | 11.69 inches (A4) |

## Usage Example

```python
from datasets import load_dataset
import base64
from io import BytesIO
from PIL import Image
import matplotlib.pyplot as plt

# Load dataset with streaming enabled
ds = load_dataset("riotu-lab/SARD", streaming=True)
print(ds)

# Iterate over a specific font dataset (e.g., Amiri)
for sample in ds["Amiri"]:
    image_name = sample["image_name"]
    chunk = sample["chunk"]  # Arabic text transcription
    font_name = sample["font_name"]
    
    # Decode Base64 image
    image_data = base64.b64decode(sample["image_base64"])
    image = Image.open(BytesIO(image_data))

    # Display the image
    plt.figure(figsize=(10, 10))
    plt.imshow(image)
    plt.axis('off')
    plt.title(f"Font: {font_name}")
    plt.show()

    # Print the details
    print(f"Image Name: {image_name}")
    print(f"Font Name: {font_name}")
    print(f"Text Chunk: {chunk}")
    
    # Break after one sample for testing
    break
```


## Applications

SAND is designed to support various Arabic text recognition tasks:

- Training and evaluating OCR models for Arabic text
- Developing vision-language models for document understanding
- Fine-tuning existing OCR models for better Arabic script recognition
- Benchmarking OCR performance across different fonts and layouts
- Research in Arabic natural language processing and computer vision



## Acknowledgments

The authors thank Prince Sultan University for their support in developing this dataset.


## Citation

If you use SARD in your work, please cite the following paper:

**APA:**

```
Nacar, O., Al-Habashi, Y., Sibaee, S., Ammar, A., & Boulila, W. (2025). SARD: A Large-Scale Synthetic Arabic OCR Dataset for Book-Style Text Recognition. arXiv preprint arXiv:2505.24600.
https://arxiv.org/abs/2505.24600

```

**BibTeX:**

```
@misc{nacar2025sard,
      title={SARD: A Large-Scale Synthetic Arabic OCR Dataset for Book-Style Text Recognition}, 
      author={Omer Nacar and Yasser Al-Habashi and Serry Sibaee and Adel Ammar and Wadii Boulila},
      year={2025},
      eprint={2505.24600},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2505.24600}, 
}

```