IDP-Leaderboard
Collection
New datasets used in Intelligent Document Processing Leaderboard. https://idp-leaderboard.org/
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8 items
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Updated
images
unknown | annotation
stringlengths 1.71k
7.04k
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"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED) | "{\"Col_1\": {\"0\": \"Zb6N\", \"1\": \"\", \"2\": \"nO3yhKdmU\", \"3\": \"\", \"4\": \"RNW2zO6f\", (...TRUNCATED) |
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED) | "{\"Col_1\": {\"0\": \"dU2dfO7gTw\", \"1\": \"rP4mxRC\", \"2\": \"xaJr\", \"3\": \"kH7WGZ1\", \"4\":(...TRUNCATED) |
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED) | "{\"Col_1\": {\"0\": \"V3cA3fyP\", \"1\": \"60AQeC\", \"2\": \"MxBkJ6\", \"3\": \"8QRnseV\", \"4\": (...TRUNCATED) |
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED) | "{\"Col_1\": {\"0\": \"NQKU3vOj\", \"1\": \"OnCz8\", \"2\": \"z2u7M57aWd\", \"3\": \"g8f75\", \"4\":(...TRUNCATED) |
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED) | "{\"Col_1\": {\"0\": \"LoN3z\", \"1\": \"gfskLK\", \"2\": \"Tocw\", \"3\": \"Dnp8\", \"4\": \"LbW\",(...TRUNCATED) |
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED) | "{\"Col_1\": {\"0\": \"Kt5rzp\", \"1\": \"\", \"2\": \"Pz3ZNk\", \"3\": \"07XOlFJMv\", \"4\": \"KRMu(...TRUNCATED) |
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED) | "{\"Col_1\": {\"0\": \"2oqqxpyWs\", \"1\": \"KaV4k9mK8\", \"2\": \"Zztww\", \"3\": \"8AZup\", \"4\":(...TRUNCATED) |
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED) | "{\"Col_1\": {\"0\": \"YXuR0Z3h\", \"1\": \"M5y8rAHyby\", \"2\": \"\", \"3\": \"fSat\", \"4\": \"40D(...TRUNCATED) |
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED) | "{\"Col_1\": {\"0\": \"Wgk\", \"1\": \"8sKFq\", \"2\": \"ubmT4hl\", \"3\": \"zehMxx4Ir\", \"4\": \"k(...TRUNCATED) |
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED) | "{\"Col_1\": {\"0\": \"\", \"1\": \"CeCcQ5vq9c\", \"2\": \"5uYCT\", \"3\": \"\", \"4\": \"\", \"5\":(...TRUNCATED) |
This dataset is generated syhthetically to create tables with following characteristics:
import io
import pandas as pd
from PIL import Image
def bytes_to_image(self, image_bytes: bytes):
return Image.open(io.BytesIO(image_bytes))
def parse_annotations(self, annotations: str) -> pd.DataFrame:
return pd.read_json(StringIO(annotations), orient="records")
test_data = load_dataset('nanonets/long_dense_structured_table', split='test')
data_point = test_data[0]
image, gt_table = (
bytes_to_image(data_point["images"]),
parse_annotations(data_point["annotation"]),
)