mapo80/DocCornerDataset

@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}
}

@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}
}

@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}
}

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

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")

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]

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()

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
)

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