Add dataset card

README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - image-segmentation
+  - keypoint-detection
+  - object-detection
+language:
+  - en
+tags:
+  - document-detection
+  - corner-detection
+  - document-scanner
+  - quadrilateral-detection
+  - perspective-correction
+  - computer-vision
+size_categories:
+  - 10K<n<100K
+---
+
+# DocCornerDataset
+
+A comprehensive dataset for **document corner detection** and **quadrilateral localization**. This dataset is designed for training models that detect the four corners of documents in natural images, enabling applications like document scanning, perspective correction, and automatic document cropping.
+
+## Dataset Description
+
+DocCornerDataset contains **27,860 images** with precise corner annotations:
+- **23,496 training samples**
+- **4,364 validation samples**
+- Includes both positive samples (with documents) and negative samples (without documents)
+
+### Key Features
+
+- **High-quality annotations**: 4-corner coordinates (TL, TR, BR, BL) in normalized format [0-1]
+- **Diverse sources**: Aggregated from multiple public datasets covering various document types
+- **Negative samples**: Non-document images to reduce false positives
+- **Pre-split data**: Ready-to-use train/validation splits
+- **Parquet format**: Efficient storage with embedded images
+
+## Dataset Structure
+
+The dataset is stored in Parquet format with the following columns:
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `image_bytes` | bytes | Raw JPEG image data |
+| `filename` | string | Original filename |
+| `has_document` | bool | True if image contains a document |
+| `x0`, `y0` | float32 | Top-left corner (normalized 0-1) |
+| `x1`, `y1` | float32 | Top-right corner (normalized 0-1) |
+| `x2`, `y2` | float32 | Bottom-right corner (normalized 0-1) |
+| `x3`, `y3` | float32 | Bottom-left corner (normalized 0-1) |
+
+## Source Datasets
+
+This dataset aggregates and re-annotates images from multiple public sources:
+
+| Source Dataset | Samples | Description |
+|----------------|---------|-------------|
+| **MIDV-500** | ~9,500 | Mobile Identity Document Video dataset |
+| **AutoCapture** | ~8,000 | Auto-captured document images |
+| **MIDV-2019** | ~1,400 | Extended mobile ID document dataset |
+| **SmartDoc-QA** | ~1,400 | Document images for QA tasks |
+| **Sample Dataset** | ~1,000 | Mixed document samples |
+| **Four Corners Detection** | ~950 | Corner detection focused dataset |
+| **Document Segmentation** | ~950 | Curated segmentation samples |
+| **ReceiptExtractor** | ~620 | Receipt and ticket images |
+| **Receipt Instance Segmentation** | ~200 | Receipt instance annotations |
+| **CORD v2** | ~80 | Consolidated Receipt Dataset |
+| **Negative Samples** | ~4,300 | Non-document background images |
+
+## Loading the Dataset
+
+### Using PyArrow/Pandas
+
+```python
+import pyarrow.parquet as pq
+import pandas as pd
+from PIL import Image
+import io
+
+# Load train data
+train_df = pd.read_parquet("hf://datasets/mapo80/DocCornerDataset/data/train_chunk000.parquet")
+
+# View a sample
+sample = train_df.iloc[0]
+image = Image.open(io.BytesIO(sample['image_bytes']))
+corners = [sample['x0'], sample['y0'], sample['x1'], sample['y1'], 
+           sample['x2'], sample['y2'], sample['x3'], sample['y3']]
+print(f"Filename: {sample['filename']}")
+print(f"Has document: {sample['has_document']}")
+print(f"Corners: {corners}")
+image.show()
+```
+
+### Using HuggingFace Datasets
+
+```python
+from datasets import load_dataset
+from PIL import Image
+import io
+
+# Load the dataset
+dataset = load_dataset("mapo80/DocCornerDataset", data_files={
+    "train": "data/train_chunk*.parquet",
+    "validation": "data/val_chunk*.parquet"
+})
+
+# View a sample
+sample = dataset["train"][0]
+image = Image.open(io.BytesIO(sample['image_bytes']))
+print(f"Filename: {sample['filename']}")
+print(f"Corners: x0={sample['x0']:.3f}, y0={sample['y0']:.3f}, ...")
+```
+
+### Using PyTorch DataLoader
+
+```python
+import torch
+from torch.utils.data import Dataset, DataLoader
+import pyarrow.parquet as pq
+from PIL import Image
+import io
+import torchvision.transforms as T
+
+class DocCornerDataset(Dataset):
+    def __init__(self, parquet_files, transform=None):
+        self.data = pq.ParquetDataset(parquet_files).read().to_pandas()
+        self.transform = transform or T.Compose([
+            T.Resize((224, 224)),
+            T.ToTensor(),
+            T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        ])
+    
+    def __len__(self):
+        return len(self.data)
+    
+    def __getitem__(self, idx):
+        row = self.data.iloc[idx]
+        image = Image.open(io.BytesIO(row['image_bytes'])).convert('RGB')
+        image = self.transform(image)
+        
+        corners = torch.tensor([
+            row['x0'], row['y0'], row['x1'], row['y1'],
+            row['x2'], row['y2'], row['x3'], row['y3']
+        ], dtype=torch.float32)
+        
+        has_doc = torch.tensor(row['has_document'], dtype=torch.float32)
+        
+        return image, corners, has_doc
+
+# Usage
+train_files = ["data/train_chunk000.parquet", "data/train_chunk001.parquet", ...]
+dataset = DocCornerDataset(train_files)
+loader = DataLoader(dataset, batch_size=32, shuffle=True)
+```
+
+## Use Cases
+
+- **Document Corner Detection**: Train models to localize document corners
+- **Document Scanning Apps**: Build automatic document capture features
+- **Perspective Correction**: Detect quadrilaterals for perspective transformation
+- **Document Segmentation**: Segment documents from background
+- **OCR Preprocessing**: Improve OCR accuracy with proper document alignment
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@dataset{doccornerdataset2024,
+  title={DocCornerDataset: A Comprehensive Dataset for Document Corner Detection},
+  author={mapo80},
+  year={2024},
+  publisher={Hugging Face},
+  url={https://huggingface.co/datasets/mapo80/DocCornerDataset}
+}
+```
+
+### Source Dataset Citations
+
+Please also consider citing the original source datasets:
+
+- **MIDV-500/2019**: Bulatov et al., "MIDV-500: A Dataset for Identity Documents Analysis and Recognition on Mobile Devices in Video Stream"
+- **SmartDoc**: Burie et al., "ICDAR 2015 Competition on Smartphone Document Capture and OCR"
+- **CORD**: Park et al., "CORD: A Consolidated Receipt Dataset for Post-OCR Parsing"
+
+## License
+
+This dataset is released under the **CC-BY-4.0** license. Please respect the licenses of the original source datasets when using this data.
+
+## Acknowledgments
+
+This dataset was created by aggregating and re-annotating images from multiple public document datasets. We thank the creators of the original datasets for making their data publicly available.