Update README.md

README.md

@@ -64,3 +64,154 @@ pretty_name: dv_page_annotation
 size_categories:
 - 10K<n<100K
 ---
+
+# 📦 Dhivehi Synthetic Document Layout + Textline Dataset
+
+This dataset contains **synthetically generated** image-document pairs with detailed layout annotations and ground-truth Dhivehi text extractions.  
+It’s designed for document layout analysis, visual document understanding, OCR fine-tuning, and related tasks specifically for Dhivehi script.
+
+***Note: this version image are compressed.***
+***Raw version 📁 **Repository**: [Hugging Face Datasets](https://huggingface.co/datasets/alakxender/od-syn-page-annotations)***
+
+## 📋 Dataset Summary
+
+- **Total Examples**: ~58,738  
+- **Image Content**: Synthetic Dhivehi documents generated to simulate real-world layouts, including headlines, textlines, pictures, and captions.  
+- **Annotations**:
+    - Bounding boxes (`bbox`)
+    - Object areas (`area`)
+    - Object categories (`category`)
+    - Ground-truth parsed text, split into:
+        - `headline` (major headings)
+        - `textline` (paragraph or text body lines)
+## ⚠️ Important Note
+
+This dataset is **synthetic** — no real-world documents or personal data were used. It was generated programmatically to train and evaluate models under controlled conditions, without legal or ethical concerns tied to real-world data.
+
+## 🏷️ Categories
+
+| Label ID | Label Name  |
+|----------|-------------|
+| 0        | Textline    |
+| 1        | Heading     |
+| 2        | Picture     |
+| 3        | Caption     |
+| 4        | Columns     |
+
+## 📁 Features
+
+| Field                | Type                                     |
+|----------------------|-----------------------------------------|
+| `image_id`          | int64                                    |
+| `image`             | image                                    |
+| `width`             | int64                                    |
+| `height`            | int64                                    |
+| `objects`           | List of:
+- `id`: int64  
+- `area`: int64  
+- `bbox`: [x, y, width, height] (float32)  
+- `category`: label (class label 0–4) |
+| `ground_truth.gt_parse` |  
+- `headline`: list of strings  
+- `textline`: list of strings |
+
+
+## 📊 Split
+
+| Split  | # Examples | Size (bytes)         |
+|--------|------------|----------------------|
+| Train  | 58,738     | ~84.31 GB (compressed) |
+
+## 📦 Download
+
+- **Download size**: ~93.32 GB  
+- **Uncompressed dataset size**: ~84.31 GB
+
+
+## 🔧 Example Use (with 🤗 Datasets)
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("alakxender/od-syn-page-annotations")
+
+categories = dataset.features["objects"].feature["category"].names
+id2label = {i: name for i, name in enumerate(categories)}
+
+print(id2label)
+
+sample = dataset['train'][0]
+print("Image ID:", sample['image_id'])
+print("Image size:", sample['width'], "x", sample['height'])
+print("First object category:", sample['objects']['category'][0])
+print("First headline:", sample['ground_truth']['gt_parse']['headline'][0])
+```
+
+## 📊 Visualize
+
+```python
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+from datasets import load_dataset
+
+def get_color(idx):
+    palette = [
+        "red", "green", "blue", "orange", "purple", "cyan", "magenta", "yellow", "lime", "pink"
+    ]
+    return palette[idx % len(palette)]
+
+def draw_bboxes(sample, id2label, save_path=None):
+    """
+    Draw bounding boxes and labels on a single dataset sample.
+
+    Args:
+        sample: A dataset example (dict) with 'image' and 'objects'.
+        id2label: Mapping from category ID to label name.
+        save_path: If provided, saves the image to this path.
+
+    Returns:
+        PIL Image with drawn bounding boxes.
+    """
+    image = sample["image"]
+    annotations = sample["objects"]
+
+    image = Image.fromarray(np.array(image))
+    draw = ImageDraw.Draw(image)
+    try:
+        font = ImageFont.truetype("arial.ttf", 14)
+    except:
+        font = ImageFont.load_default()
+
+    for category, box in zip(annotations["category"], annotations["bbox"]):
+        x, y, w, h = box
+        color = get_color(category)
+        draw.rectangle((x, y, x + w, y + h), outline=color, width=2)
+        label = id2label[category]
+        bbox = font.getbbox(label)
+        text_width = bbox[2] - bbox[0]
+        text_height = bbox[3] - bbox[1]
+        draw.rectangle([x, y, x + text_width + 4, y + text_height + 2], fill=color)
+        draw.text((x + 2, y + 1), label, fill="black", font=font)
+
+    if save_path:
+        image.save(save_path)
+        print(f"Saved image to {save_path}")
+    else:
+        image.show()
+
+    return image
+
+# Load one sample
+dataset = load_dataset("alakxender/od-syn-page-annotations", split="train[:1]")
+
+# Get category mapping
+categories = dataset.features["objects"].feature["category"].names
+id2label = {i: name for i, name in enumerate(categories)}
+
+# Draw bounding boxes on the first sample
+draw_bboxes(
+    sample=dataset[0],
+    id2label=id2label,
+    save_path="sample_0.png"
+)
+```