Update README.md

README.md

@@ -19,4 +19,58 @@ features:
     dtype: string
 tags:
 - dataset
----
+---
+
+### Dataset Description
+
+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.
+
+### 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.
+
+- **font_name**: The font used in text rendering.
+
+- **image_base64**: Base64-encoded image representation.
+
+### How to Use
+
+```python
+from datasets import load_dataset
+import base64
+from io import BytesIO
+from PIL import Image
+# Load dataset with streaming enabled
+ds = load_dataset("xya22er/text_to_image", 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"]
+    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))
+
+    # Show the image (optional)
+    image.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
+```