Datasets:

mapo80
/

DocCornerDataset

@@ -1,193 +0,0 @@
----
-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.