--- 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 ### Validation Set Validation samples ### Test Set Test samples *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