Update README.md

README.md

@@ -34,14 +34,14 @@ size_categories:
 - 100K<n<1M
 ---
 
-# SAND: Large-Scale Synthetic Arabic OCR Dataset
+# 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
 
-**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.
+**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
 
@@ -129,7 +129,7 @@ from PIL import Image
 import matplotlib.pyplot as plt
 
 # Load dataset with streaming enabled
-ds = load_dataset("riotu-lab/SAND", streaming=True)
+ds = load_dataset("riotu-lab/SARD", streaming=True)
 print(ds)
 
 # Iterate over a specific font dataset (e.g., Amiri)
@@ -169,18 +169,7 @@ SAND is designed to support various Arabic text recognition tasks:
 - 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