Update README.md

README.md

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 - n>1T
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-### Dataset Description
+# SAND: Large-Scale Synthetic Arabic OCR Dataset
 
-This dataset is designed for training and evaluating Optical Character Recognition (OCR) models 
-for Arabic text. It is an extension of an open-source dataset and includes text rendered in multiple Arabic fonts (Amiri, Sakkal Majalla, Arial, Calibri and Scheherazade New). 
-The dataset simulates real-world book layouts to enhance OCR accuracy.
+[![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)
 
-### Dataset Structure
-The dataset is divided into five splits based on font name (Sakkal_Majalla, Amiri, Arial, Calibri, and Scheherazade_New). 
-Each split contains data specific to a single font. Within each split, the following attributes are present:
-- **image_name**: Unique identifier for each image.
-- **chunk**: The text content associated with the image.
+## Overview
 
-- **font_name**: The font used in text rendering.
+**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.
 
-- **image_base64**: Base64-encoded image representation.
+## Key Features
 
-### How to Use
+- **Massive Scale**: 743,000 document images containing 662.15 million words
+- **Typographic Diversity**: Five distinct Arabic fonts (Amiri, Sakkal Majalla, Arial, Calibri, and Scheherazade New)
+- **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**: ~148,541 document images
+- **Sakkal Majalla**: ~148,541 document images
+- **Arial**: ~148,541 document images
+- **Calibri**: ~148,541 document images
+- **Scheherazade New**: ~148,541 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
+
+## 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 |
+
+## 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/text2image", streaming=True)
+ds = load_dataset("riotu-lab/SAND", streaming=True)
 print(ds)
 
-# Load the dataset
-
 # Iterate over a specific font dataset (e.g., Amiri)
 for sample in ds["Amiri"]:
     image_name = sample["image_name"]
@@ -73,8 +142,12 @@ for sample in ds["Amiri"]:
     image_data = base64.b64decode(sample["image_base64"])
     image = Image.open(BytesIO(image_data))
 
-    # Show the image (optional)
-    image.show()
+    # 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}")
@@ -85,5 +158,31 @@ for sample in ds["Amiri"]:
     break
 ```
 
-# OCR Dataset Generation Pipeline
-To create your own dataset, you can use the following repository: [text2image](https://github.com/riotu-lab/text2image).
+
+## 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
+
+## Citation
+
+If you use SAND in your research, please cite:
+
+```bibtex
+@misc{sand2025,
+  title={SAND: A Large-Scale Synthetic Arabic OCR Corpus 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.
+