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README.md

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+---
+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

dataset_info.json

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+{
+  "description": "Document Corner Detection Dataset - Best generalist training data from doc-scanner-dataset-rev-new with train_clean_iter3_plus_hard_full, val_clean_iter3, and test splits.",
+  "citation": "",
+  "homepage": "https://huggingface.co/datasets/mapo80/DocCornerDataset",
+  "license": "MIT",
+  "features": {
+    "image": {
+      "_type": "Image"
+    },
+    "filename": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "is_negative": {
+      "dtype": "bool",
+      "_type": "Value"
+    },
+    "corner_tl_x": {
+      "dtype": "float32",
+      "_type": "Value"
+    },
+    "corner_tl_y": {
+      "dtype": "float32",
+      "_type": "Value"
+    },
+    "corner_tr_x": {
+      "dtype": "float32",
+      "_type": "Value"
+    },
+    "corner_tr_y": {
+      "dtype": "float32",
+      "_type": "Value"
+    },
+    "corner_br_x": {
+      "dtype": "float32",
+      "_type": "Value"
+    },
+    "corner_br_y": {
+      "dtype": "float32",
+      "_type": "Value"
+    },
+    "corner_bl_x": {
+      "dtype": "float32",
+      "_type": "Value"
+    },
+    "corner_bl_y": {
+      "dtype": "float32",
+      "_type": "Value"
+    }
+  },
+  "splits": {
+    "train": {
+      "name": "train",
+      "num_examples": 32968
+    },
+    "validation": {
+      "name": "validation",
+      "num_examples": 8645
+    },
+    "test": {
+      "name": "test",
+      "num_examples": 6652
+    }
+  },
+  "dataset_name": "DocCornerDataset-BestGeneralist",
+  "config_name": "default"
+}

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