README.md

---
dataset_info:
  features:
    - name: image
      dtype: image
    - name: filename
      dtype: string
    - name: is_negative
      dtype: bool
    - name: corner_tl_x
      dtype: float32
    - name: corner_tl_y
      dtype: float32
    - name: corner_tr_x
      dtype: float32
    - name: corner_tr_y
      dtype: float32
    - name: corner_br_x
      dtype: float32
    - name: corner_br_y
      dtype: float32
    - name: corner_bl_x
      dtype: float32
    - name: corner_bl_y
      dtype: float32
  splits:
    - name: train
      num_examples: 32968
    - name: validation
      num_examples: 8645
    - name: test
      num_examples: 6652
configs:
  - config_name: default
    data_files:
      - split: train
        path: train/*.parquet
      - split: validation
        path: val/*.parquet
      - split: test
        path: test/*.parquet
license: other
task_categories:
  - image-segmentation
  - keypoint-detection
  - object-detection
tags:
  - document-detection
  - corner-detection
  - perspective-correction
  - document-scanner
  - keypoint-regression
language:
  - en
size_categories:
  - 10K<n<100K
---

# DocCornerDataset

A high-quality document corner detection dataset for training models to detect the four corners of documents in images. This dataset is optimized for building robust document scanning and perspective correction applications.

## Dataset Examples

### Training Set
<img src="collages/train_collage.jpg" alt="Training samples" width="600"/>

### Validation Set
<img src="collages/val_collage.jpg" alt="Validation samples" width="600"/>

### Test Set
<img src="collages/test_collage.jpg" alt="Test samples" width="600"/>

*Green polygons show the annotated document corners*

## Dataset Description

This dataset contains images with document corner annotations, optimized for training robust document detection models. It uses the best-performing splits from an iterative dataset cleaning process with multiple quality validation steps.

### Key Features

- **High Quality Annotations**: Labels refined through iterative cleaning with multiple teacher models
- **Diverse Document Types**: IDs, invoices, receipts, books, cards, and general documents
- **Negative Samples**: Includes images without documents for training robust classifiers
- **No Overlap**: Train, validation, and test splits are completely disjoint

## Dataset Statistics

| Split | Images | Description |
|-------|--------|-------------|
| `train` | 32,968 | Training set (cleaned iter3 + hard negatives) |
| `validation` | 8,645 | Validation set (cleaned iter3) |
| `test` | 6,652 | Held-out test set (no overlap with train/val) |
| **Total** | **48,265** | |

## Data Sources and Licenses

This dataset is compiled from multiple open-source datasets. **Please refer to the original dataset licenses before using this data.**

### MIDV Dataset (ID Cards)

Mobile Identity Document Video dataset for identity document detection and recognition.

| Dataset | Images | License | Source |
|---------|--------|---------|--------|
| **MIDV-500** | ~9,400 | Research use | [Website](http://l3i-share.univ-lr.fr/MIDV500/) |
| **MIDV-2019** | ~1,350 | Research use | [Website](http://l3i-share.univ-lr.fr/MIDV2019/) |

**Citation:**
```bibtex
@article{arlazarov2019midv500,
  title={MIDV-500: A Dataset for Identity Documents Analysis and Recognition on Mobile Devices in Video Stream},
  author={Arlazarov, V.V. and Bulatov, K. and Chernov, T. and Arlazarov, V.L.},
  journal={Computer Optics},
  volume={43},
  number={5},
  pages={818--824},
  year={2019}
}

@inproceedings{arlazarov2019midv2019,
  title={MIDV-2019: Challenges of the modern mobile-based document OCR},
  author={Arlazarov, V.V. and Bulatov, K. and Chernov, T. and Arlazarov, V.L.},
  booktitle={ICDAR},
  year={2019}
}
```

### SmartDoc Dataset (Documents)

SmartDoc Challenge dataset for document image acquisition and quality assessment.

| Dataset | Images | License | Source |
|---------|--------|---------|--------|
| **SmartDoc** | ~1,380 | Research use | [Website](https://smartdoc.univ-lr.fr/) |

**Citation:**
```bibtex
@inproceedings{burie2015smartdoc,
  title={ICDAR 2015 Competition on Smartphone Document Capture and OCR (SmartDoc)},
  author={Burie, J.C. and Chazalon, J. and Coustaty, M. and others},
  booktitle={ICDAR},
  year={2015}
}
```

### COCO Dataset (Negative Samples)

Common Objects in Context dataset used for negative samples (images without documents).

| Dataset | Images | License | Source |
|---------|--------|---------|--------|
| **COCO val2017** | ~4,300 | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | [Website](https://cocodataset.org/) |
| **COCO train2017** | ~11,400 | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | [Website](https://cocodataset.org/) |

**Note:** Excluded categories that could be confused with documents: book, laptop, tv, cell phone, keyboard, mouse, remote, clock.

**Citation:**
```bibtex
@inproceedings{lin2014coco,
  title={Microsoft COCO: Common Objects in Context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and others},
  booktitle={ECCV},
  year={2014}
}
```

### Roboflow Universe (Various Documents)

Various document datasets from Roboflow Universe community.

| Category | Datasets | License | Source |
|----------|----------|---------|--------|
| **Documents** | document_segmentation_v2, doc_scanner, doc_rida, documento | Various (check individual) | [Roboflow Universe](https://universe.roboflow.com/) |
| **Bills/Invoices** | bill_segmentation, cs_invoice | Various (check individual) | [Roboflow Universe](https://universe.roboflow.com/) |
| **Receipts** | receipt_detection, receipt_occam, receipts_coolstuff | Various (check individual) | [Roboflow Universe](https://universe.roboflow.com/) |
| **ID Cards** | card_corner, card_4_class, id_card_skew, id_detections, idcard_jj | Various (check individual) | [Roboflow Universe](https://universe.roboflow.com/) |
| **Passports** | segment_passport | Various (check individual) | [Roboflow Universe](https://universe.roboflow.com/) |
| **Books** | book_reader, page_segmentation_tecgp, book_cmjt2 | Various (check individual) | [Roboflow Universe](https://universe.roboflow.com/) |

**Note:** Roboflow datasets have various licenses. Please check the individual dataset pages on [Roboflow Universe](https://universe.roboflow.com/) for specific license terms.

## Features

| Feature | Type | Description |
|---------|------|-------------|
| `image` | Image | The document image (JPEG) |
| `filename` | string | Original filename for traceability |
| `is_negative` | bool | `True` if image contains no document |
| `corner_tl_x` | float32 | Top-left corner X coordinate (normalized 0-1) |
| `corner_tl_y` | float32 | Top-left corner Y coordinate (normalized 0-1) |
| `corner_tr_x` | float32 | Top-right corner X coordinate (normalized 0-1) |
| `corner_tr_y` | float32 | Top-right corner Y coordinate (normalized 0-1) |
| `corner_br_x` | float32 | Bottom-right corner X coordinate (normalized 0-1) |
| `corner_br_y` | float32 | Bottom-right corner Y coordinate (normalized 0-1) |
| `corner_bl_x` | float32 | Bottom-left corner X coordinate (normalized 0-1) |
| `corner_bl_y` | float32 | Bottom-left corner Y coordinate (normalized 0-1) |

### Corner Order

Corners are ordered **clockwise** starting from top-left:

```
1 (TL) -------- 2 (TR)
   |              |
   |   Document   |
   |              |
4 (BL) -------- 3 (BR)
```

### Coordinate System

- Coordinates are **normalized** to the range [0, 1]
- To convert to pixel coordinates: `pixel_x = corner_x * image_width`
- Origin (0, 0) is at the **top-left** of the image

### Negative Samples

Images with `is_negative=True`:
- Do not contain any document
- All corner coordinates are `null`
- Useful for training classifiers to reject non-document images

## Usage

### Loading the Dataset

```python
from datasets import load_dataset

# Load all splits
dataset = load_dataset("mapo80/DocCornerDataset")

# Access specific splits
train_data = dataset["train"]
val_data = dataset["validation"]
test_data = dataset["test"]

print(f"Train: {len(train_data)} samples")
print(f"Val: {len(val_data)} samples")
print(f"Test: {len(test_data)} samples")
```

### Iterating Over Samples

```python
for sample in dataset["train"]:
    image = sample["image"]  # PIL Image
    filename = sample["filename"]

    if not sample["is_negative"]:
        # Get corner coordinates (normalized 0-1)
        corners = [
            (sample["corner_tl_x"], sample["corner_tl_y"]),
            (sample["corner_tr_x"], sample["corner_tr_y"]),
            (sample["corner_br_x"], sample["corner_br_y"]),
            (sample["corner_bl_x"], sample["corner_bl_y"]),
        ]

        # Convert to pixel coordinates
        w, h = image.size
        corners_px = [(int(x * w), int(y * h)) for x, y in corners]
```

### Visualizing Annotations

```python
from PIL import Image, ImageDraw

def draw_corners(image, corners, color=(0, 255, 0), width=3):
    """Draw document corners on image."""
    draw = ImageDraw.Draw(image)
    w, h = image.size

    # Convert normalized to pixel coords
    points = [(int(c[0] * w), int(c[1] * h)) for c in corners]

    # Draw polygon
    for i in range(4):
        draw.line([points[i], points[(i+1) % 4]], fill=color, width=width)

    # Draw corner circles
    for p in points:
        r = 5
        draw.ellipse([p[0]-r, p[1]-r, p[0]+r, p[1]+r], fill=color)

    return image

# Example usage
sample = dataset["train"][0]
if not sample["is_negative"]:
    corners = [
        (sample["corner_tl_x"], sample["corner_tl_y"]),
        (sample["corner_tr_x"], sample["corner_tr_y"]),
        (sample["corner_br_x"], sample["corner_br_y"]),
        (sample["corner_bl_x"], sample["corner_bl_y"]),
    ]
    annotated = draw_corners(sample["image"].copy(), corners)
    annotated.show()
```

### Training a Model (PyTorch Example)

```python
import torch
from torch.utils.data import DataLoader
from datasets import load_dataset

dataset = load_dataset("mapo80/DocCornerDataset")

def collate_fn(batch):
    images = torch.stack([transform(s["image"]) for s in batch])

    # Stack corner coordinates (8 values per sample)
    corners = []
    for s in batch:
        if s["is_negative"]:
            corners.append(torch.zeros(8))
        else:
            corners.append(torch.tensor([
                s["corner_tl_x"], s["corner_tl_y"],
                s["corner_tr_x"], s["corner_tr_y"],
                s["corner_br_x"], s["corner_br_y"],
                s["corner_bl_x"], s["corner_bl_y"],
            ]))

    return images, torch.stack(corners)

train_loader = DataLoader(
    dataset["train"],
    batch_size=32,
    shuffle=True,
    collate_fn=collate_fn
)
```

## Model Performance

Models trained on this dataset achieve the following performance:

| Model | Input Size | mIoU (val) | mIoU (test) |
|-------|------------|------------|-------------|
| MobileNetV2 (alpha=0.35) | 224x224 | 0.9894 | 0.9826 |
| MobileNetV2 (alpha=0.35) | 256x256 | 0.9902 | 0.9819 |

*mIoU = Mean Intersection over Union between predicted and ground truth quadrilaterals*

## Citation

If you use this dataset in your research, please cite this dataset and the original source datasets:

```bibtex
@dataset{doccornerdataset2025,
  author = {mapo80},
  title = {DocCornerDataset: Document Corner Detection Dataset},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/mapo80/DocCornerDataset}
}
```

**Please also cite the original datasets used:**
- MIDV-500/MIDV-2019 (Arlazarov et al., 2019)
- SmartDoc (Burie et al., 2015)
- COCO (Lin et al., 2014)

## License

⚠️ **This dataset is compiled from multiple sources with different licenses.**

| Source | License |
|--------|---------|
| MIDV-500/MIDV-2019 | Research use only |
| SmartDoc | Research use only |
| COCO | CC BY 4.0 |
| Roboflow datasets | Various (check individual datasets) |

**Before using this dataset, please review the licenses of the original datasets:**
- [MIDV-500](http://l3i-share.univ-lr.fr/MIDV500/)
- [MIDV-2019](http://l3i-share.univ-lr.fr/MIDV2019/)
- [SmartDoc](https://smartdoc.univ-lr.fr/)
- [COCO](https://cocodataset.org/#termsofuse)
- [Roboflow Universe](https://universe.roboflow.com/) (check individual datasets)

## Acknowledgments

This dataset was created by combining and processing multiple open-source datasets. We thank the authors of MIDV, SmartDoc, COCO, and the Roboflow community for making their data available.

## Related Projects

- [DocCornerNet](https://github.com/mapo80/DocCornerNet-CoordClass) - Document corner detection model trained on this dataset