README.md

---
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.