README.md

---
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: Jozoor_Font
    path: Jozoor_Font/*.csv
  - split: Al_Jazeera_Arabic_Regular
    path: Al_Jazeera_Arabic_Regular/*.csv
  - split: Lateef
    path: Lateef/*.csv
  - split: Noto_Naskh_Arabic_UI
    path: Noto_Naskh_Arabic_UI/*.csv
  - split: Thabit
    path: Thabit/*.csv
features:
  text:
    dtype: string
tags:
- dataset
- OCR
- Arabic
- Image-to-text
license: apache-2.0
pretty_name: >-
  SAND-Extended: A Large-Scale Synthetic Arabic OCR Corpus for Vision-Language
  Models
task_categories:
- image-to-text
---

# SAND: A Large-Scale Synthetic Arabic OCR Dataset

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

## Overview

**SAND** (Synthetic Arabic OCR 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**: 843,622 document images containing approximately 690 million words
- **Extensive Typographic Diversity**: Ten distinct Arabic fonts covering a wide range of styles
- **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 ten splits based on font name:

- **Amiri**: Classical Naskh typeface inspired by early 20th century typography
- **Sakkal Majalla**: Widely used font in contemporary Arabic publishing
- **Arial**: Modern sans-serif font common in digital publications
- **Calibri**: Microsoft's default font representing contemporary digital typography
- **Scheherazade New**: Traditional-style font based on classical manuscript styles
- **Jozoor Font**: Decorative Arabic font with more stylized character forms
- **Lateef**: Extended Arabic script font designed for readability at small sizes
- **Noto Naskh Arabic UI**: Part of Google's Noto family, designed for user interfaces
- **Thabit**: Monospaced Arabic font for technical documentation
- **Al Jazeera Arabic Regular**: Based on the typography used by Al Jazeera media

📋 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

## 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 | Characteristics |
|------|----------------|-----------|-----------------|
| Sakkal Majalla | 50–300 | 14 pt | Contemporary publishing style |
| Arial | 50–500 | 12 pt | Modern sans-serif |
| Calibri | 50–500 | 12 pt | Contemporary digital document |
| Amiri | 50–300 | 12 pt | Classical Naskh typeface |
| Scheherazade New | 50–250 | 12 pt | Traditional manuscript style |
| Noto Naskh Arabic UI | 50–400 | 12 pt | Clear UI rendering |
| Lateef | 50–350 | 14 pt | Optimized for small sizes |
| Al-Jazeera-Arabic | 50–250 | 12 pt | Media/journalistic style |
| Thabit | 50–240 | 12 pt | Monospaced technical font |
| Jozoor Font | 50–200 | 12 pt | Decorative with stylization |

## Page Layout

| Specification | Measurement |
|---------------|-------------|
| Page Size | A4 (8.27 × 11.69 in) |
| Left Margin | 0.9 in |
| Right Margin | 0.9 in |
| Top Margin | 1.0 in |
| Bottom Margin | 1.0 in |
| Gutter Margin | 0.2 in |
| Resolution | 300 DPI |
| Color Mode | Grayscale |
| Page Direction | Right-to-Left |
| Text Alignment | Right |
| Line Spacing | 1.15 |

## 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/SAND-Extended", 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
```

## Working with Multiple Fonts

To train or evaluate models across different font styles:

```python
from datasets import load_dataset
import random

# Load the dataset
ds = load_dataset("riotu-lab/SAND")

# Select a balanced sample from multiple fonts
fonts_to_use = ["Amiri", "Arial", "Scheherazade_New", "Thabit", "Noto_Naskh_Arabic_UI"]
samples_per_font = 1000
combined_samples = []

for font in fonts_to_use:
    # Get random samples from this font
    font_samples = ds[font].shuffle(seed=42).select(range(samples_per_font))
    combined_samples.extend([(sample, font) for sample in font_samples])

# Shuffle the combined samples
random.shuffle(combined_samples)

# Now you can use these samples for training or evaluation
for sample, font in combined_samples[:5]:  # Just show first 5 as example
    print(f"Font: {font}, Image: {sample['image_name']}")
```


## 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
- Developing font-adaptive OCR systems that generalize across typographic styles

## Citation

If you use SAND in your research, please cite:

```bibtex
@misc{sand2025,
  title={SAND: A Large-Scale Synthetic Arabic OCR Dataset for Vision-Language Models},
  author={RIOTU Lab},
  year={2025},
  howpublished={\url{https://huggingface.co/datasets/riotu-lab/SAND}}
}
```

## Acknowledgments

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